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BackgroundConversational agents (CAs) for chronic disease management are receiving increasing attention in academia and the industry. However, long-term adherence to CAs is still a challenge and needs to be explored. Personalization of CAs has the potential to improve long-term adherence and, with it, user satisfaction, task efficiency, perceived benefits, and intended behavior change. Research on personalized CAs has already addressed different aspects, such as personalized recommendations and anthropomorphic cues. However, detailed information on interaction styles between patients and CAs in the role of medical health care professionals is scant. Such interaction styles play essential roles for patient satisfaction, treatment adherence, and outcome, as has been shown for physician-patient interactions. Currently, it is not clear (1) whether chronically ill patients prefer a CA with a paternalistic, informative, interpretive, or deliberative interaction style, and (2) which factors influence these preferences. ObjectiveWe aimed to investigate the preferences of chronically ill patients for CA-delivered interaction styles. MethodsWe conducted two studies. The first study included a paper-based approach and explored the preferences of chronic obstructive pulmonary disease (COPD) patients for paternalistic, informative, interpretive, and deliberative CA-delivered interaction styles. Based on these results, a second study assessed the effects of the paternalistic and deliberative interaction styles on the relationship quality between the CA and patients via hierarchical multiple linear regression analyses in an online experiment with COPD patients. Patients’ sociodemographic and disease-specific characteristics served as moderator variables. ResultsStudy 1 with 117 COPD patients revealed a preference for the deliberative (50/117) and informative (34/117) interaction styles across demographic characteristics. All patients who preferred the paternalistic style over the other interaction styles had more severe COPD (three patients, Global Initiative for Chronic Obstructive Lung Disease class 3 or 4). In Study 2 with 123 newly recruited COPD patients, younger participants and participants with a less recent COPD diagnosis scored higher on interaction-related outcomes when interacting with a CA that delivered the deliberative interaction style (interaction between age and CA type: relationship quality: b=−0.77, 95% CI −1.37 to −0.18; intention to continue interaction: b=−0.49, 95% CI −0.97 to −0.01; working alliance attachment bond: b=−0.65, 95% CI −1.26 to −0.04; working alliance goal agreement: b=−0.59, 95% CI −1.18 to −0.01; interaction between recency of COPD diagnosis and CA type: working alliance goal agreement: b=0.57, 95% CI 0.01 to 1.13). ConclusionsOur results indicate that age and a patient’s personal disease experience inform which CA interaction style the patient should be paired with to achieve increased interaction-related outcomes with the CA. These results allow the design of personalized health care CAs with the goal to increase long-term adherence to health-promoting behavior.

Acquiring diagnostic ultrasound competencies and skills is crucial in modern health care, and achieving the practical experience is vital in developing the necessary anatomy interpretation and scan acquisition skills. However, traditional teaching methods may not be sufficient to provide hands-on practice, which is essential for this skill acquisition. This paper explores various modalities and instructors involved in ultrasound education to identify the most effective approaches. The field of ultrasound instruction is enriched by the diverse roles of physicians, anatomists, peer tutors, and sonographers. All these healthcare professionals can inspire and empower the next generation of ultrasound practitioners with continuous training and support. Physicians bring their clinical expertise to the table, while anatomists enhance the understanding of anatomical knowledge through ultrasound integration. Peer tutors, often medical students, provide a layer of social congruence and motivation to the learning process. Sonographers provide intensive practical experience and structured learning plans to students. By combining different instructors and teaching methods, success can be achieved in ultrasound education. An ultrasound curriculum organized by experts in the field can lead to more efficient use of resources and better learning outcomes. Empowering students through peer-assisted learning can also ease the burden on faculty. Every instructor must receive comprehensive didactic training to ensure high-quality education in diagnostic ultrasound., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

As an extension of the clinical examination and as a diagnostic and problem-solving tool, ultrasound has become an established technique for clinicians. A prerequisite for high-quality clinical ultrasound practice is adequate student ultrasound training. In light of the considerable heterogeneity of ultrasound curricula in medical studies worldwide, this review presents basic principles of modern medical student ultrasound education and advocates for the establishment of an ultrasound core curriculum embedded both horizontally and vertically in medical studies., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Background: The current paper details findings from Elena+: Care for COVID-19, an app developed to tackle the collateral damage of lockdowns and social distancing, by offering pandemic lifestyle coaching across seven health areas: anxiety, loneliness, mental resources, sleep, diet and nutrition, physical activity, and COVID-19 information., Methods: The Elena+ app functions as a single-arm interventional study, with participants recruited predominantly via social media. We used paired samples T -tests and within subjects ANOVA to examine changes in health outcome assessments and user experience evaluations over time. To investigate the mediating role of behavioral activation (i.e., users setting behavioral intentions and reporting actual behaviors) we use mixed-effect regression models. Free-text entries were analyzed qualitatively., Results: Results show strong demand for publicly available lifestyle coaching during the pandemic, with total downloads ( N = 7'135) and 55.8% of downloaders opening the app ( n = 3,928) with 9.8% completing at least one subtopic ( n = 698). Greatest areas of health vulnerability as assessed with screening measures were physical activity with 62% ( n = 1,000) and anxiety with 46.5% ( n = 760). The app was effective in the treatment of mental health; with a significant decrease in depression between first (14 days), second (28 days), and third (42 days) assessments: F 2,38 = 7.01, p = 0.003, with a large effect size (η2G = 0.14), and anxiety between first and second assessments: t 54 = 3.7, p = <0.001 with a medium effect size (Cohen d = 0.499). Those that followed the coaching program increased in net promoter score between the first and second assessment: t 36 = 2.08, p = 0.045 with a small to medium effect size (Cohen d = 0.342). Mediation analyses showed that while increasing number of subtopics completed increased behavioral activation (i.e., match between behavioral intentions and self-reported actual behaviors), behavioral activation did not mediate the relationship to improvements in health outcome assessments., Conclusions: Findings show that: (i) there is public demand for chatbot led digital coaching, (ii) such tools can be effective in delivering treatment success, and (iii) they are highly valued by their long-term user base. As the current intervention was developed at rapid speed to meet the emergency pandemic context, the future looks bright for other public health focused chatbot-led digital health interventions., Competing Interests: EF and TK are cofounders of Pathmate Technologies, a university spin-off company that creates and delivers digital clinical pathways. Pathmate Technologies was not involved in the study design, methods, or results of this paper., (Copyright © 2023 Ollier, Suryapalli, Fleisch, Wangenheim, Mair, Salamanca-Sanabria and Kowatsch.)

Background: Non-communicable diseases (NCDs) and common mental disorders (CMDs) are the leading causes of death and disability worldwide. Lifestyle interventions via mobile apps and conversational agents present themselves as low-cost, scalable solutions to prevent these conditions. This paper describes the rationale for, and development of, "LvL UP 1.0″, a smartphone-based lifestyle intervention aimed at preventing NCDs and CMDs., Materials and Methods: A multidisciplinary team led the intervention design process of LvL UP 1.0, involving four phases: (i) preliminary research (stakeholder consultations, systematic market reviews), (ii) selecting intervention components and developing the conceptual model, (iii) whiteboarding and prototype design, and (iv) testing and refinement. The Multiphase Optimization Strategy and the UK Medical Research Council framework for developing and evaluating complex interventions were used to guide the intervention development., Results: Preliminary research highlighted the importance of targeting holistic wellbeing (i.e., both physical and mental health). Accordingly, the first version of LvL UP features a scalable, smartphone-based, and conversational agent-delivered holistic lifestyle intervention built around three pillars: Move More (physical activity), Eat Well (nutrition and healthy eating), and Stress Less (emotional regulation and wellbeing). Intervention components include health literacy and psychoeducational coaching sessions, daily "Life Hacks" (healthy activity suggestions), breathing exercises, and journaling. In addition to the intervention components, formative research also stressed the need to introduce engagement-specific components to maximise uptake and long-term use. LvL UP includes a motivational interviewing and storytelling approach to deliver the coaching sessions, as well as progress feedback and gamification. Offline materials are also offered to allow users access to essential intervention content without needing a mobile device., Conclusions: The development process of LvL UP 1.0 led to an evidence-based and user-informed smartphone-based intervention aimed at preventing NCDs and CMDs. LvL UP is designed to be a scalable, engaging, prevention-oriented, holistic intervention for adults at risk of NCDs and CMDs. A feasibility study, and subsequent optimisation and randomised-controlled trials are planned to further refine the intervention and establish effectiveness. The development process described here may prove helpful to other intervention developers., Competing Interests: FvW, TK, and EF are affiliated with the Center for Digital Health Interventions, a joint initiative of the Department of Management, Technology, and Economics at ETH Zurich and the Institute of Technology Management at the University of St Gallen, which is funded in part by CSS, a Swiss health insurer. TK and EF are also the founders of Pathmate Technologies, a university spin-off company that creates and delivers digital clinical pathways. However, Pathmate Technologies was not involved in any way in the design, interpretation, and analysis during the study, or in writing the paper. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Castro, Mair, Salamanca-Sanabria, Alattas, Keller, Zheng, Jabir, Lin, Frese, Lim, Santhanam, van Dam, Car, Lee, Tai, Fleisch, von Wangenheim, Tudor Car, Müller-Riemenschneider and Kowatsch.)

Background: Changing lifestyle patterns over the last decades have seen growing numbers of people in Asia affected by non-communicable diseases and common mental health disorders, including diabetes, cancer, and/or depression. Interventions targeting healthy lifestyle behaviours through mobile technologies, including new approaches such as chatbots, may be an effective, low-cost approach to prevent these conditions. To ensure uptake and engagement with mobile health interventions, however, it is essential to understand the end-users' perspectives on using such interventions. The aim of this study was to explore perceptions, barriers, and facilitators to the use of mobile health interventions for lifestyle behaviour change in Singapore., Methods: Six virtual focus group discussions were conducted with a total of 34 participants (mean ± SD; aged 45 ± 3.6 years; 64.7% females). Focus group recordings were transcribed verbatim and analysed using an inductive thematic analysis approach, followed by deductive mapping according to perceptions, barriers, facilitators, mixed factors, or strategies., Results: Five themes were identified: (i) holistic wellbeing is central to healthy living (i.e., the importance of both physical and mental health); (ii) encouraging uptake of a mobile health intervention is influenced by factors such as incentives and government backing; (iii) trying out a mobile health intervention is one thing, sticking to it long term is another and there are key factors, such as personalisation and ease of use that influence sustained engagement with mobile health interventions; (iv) perceptions of chatbots as a tool to support healthy lifestyle behaviour are influenced by previous negative experiences with chatbots, which might hamper uptake; and (v) sharing health-related data is OK, but with conditions such as clarity on who will have access to the data, how it will be stored, and for what purpose it will be used., Conclusions: Findings highlight several factors that are relevant for the development and implementation of mobile health interventions in Singapore and other Asian countries. Recommendations include: (i) targeting holistic wellbeing, (ii) tailoring content to address environment-specific barriers, (iii) partnering with government and/or local (non-profit) institutions in the development and/or promotion of mobile health interventions, (iv) managing expectations regarding the use of incentives, and (iv) identifying potential alternatives or complementary approaches to the use of chatbots, particularly for mental health., (© 2023. The Author(s).)

Background: Work stress places a heavy economic and disease burden on society. Recent technological advances include digital health interventions for helping employees prevent and manage their stress at work effectively. Although such digital solutions come with an array of ethical risks, especially if they involve biomedical big data, the incorporation of employees' values in their design and deployment has been widely overlooked., Objective: To bridge this gap, we used the value sensitive design (VSD) framework to identify relevant values concerning a digital stress management intervention (dSMI) at the workplace, assess how users comprehend these values, and derive specific requirements for an ethics-informed design of dSMIs. VSD is a theoretically grounded framework that front-loads ethics by accounting for values throughout the design process of a technology., Methods: We conducted a literature search to identify relevant values of dSMIs at the workplace. To understand how potential users comprehend these values and derive design requirements, we conducted a web-based study that contained closed and open questions with employees of a Swiss company, allowing both quantitative and qualitative analyses., Results: The values health and well-being, privacy, autonomy, accountability, and identity were identified through our literature search. Statistical analysis of 170 responses from the web-based study revealed that the intention to use and perceived usefulness of a dSMI were moderate to high. Employees' moderate to high health and well-being concerns included worries that a dSMI would not be effective or would even amplify their stress levels. Privacy concerns were also rated on the higher end of the score range, whereas concerns regarding autonomy, accountability, and identity were rated lower. Moreover, a personalized dSMI with a monitoring system involving a machine learning-based analysis of data led to significantly higher privacy (P=.009) and accountability concerns (P=.04) than a dSMI without a monitoring system. In addition, integrability, user-friendliness, and digital independence emerged as novel values from the qualitative analysis of 85 text responses., Conclusions: Although most surveyed employees were willing to use a dSMI at the workplace, there were considerable health and well-being concerns with regard to effectiveness and problem perpetuation. For a minority of employees who value digital independence, a nondigital offer might be more suitable. In terms of the type of dSMI, privacy and accountability concerns must be particularly well addressed if a machine learning-based monitoring component is included. To help mitigate these concerns, we propose specific requirements to support the VSD of a dSMI at the workplace. The results of this work and our research protocol will inform future research on VSD-based interventions and further advance the integration of ethics in digital health., (©Jasmine I Kerr, Mara Naegelin, Michaela Benk, Florian v Wangenheim, Erika Meins, Eleonora Viganò, Andrea Ferrario. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.04.2023.)

Background and Objective: Work-related stress affects a large part of today's workforce and is known to have detrimental effects on physical and mental health. Continuous and unobtrusive stress detection may help prevent and reduce stress by providing personalised feedback and allowing for the development of just-in-time adaptive health interventions for stress management. Previous studies on stress detection in work environments have often struggled to adequately reflect real-world conditions in controlled laboratory experiments. To close this gap, in this paper, we present a machine learning methodology for stress detection based on multimodal data collected from unobtrusive sources in an experiment simulating a realistic group office environment (N=90)., Methods: We derive mouse, keyboard and heart rate variability features to detect three levels of perceived stress, valence and arousal with support vector machines, random forests and gradient boosting models using 10-fold cross-validation. We interpret the contributions of features to the model predictions with SHapley Additive exPlanations (SHAP) value plots., Results: The gradient boosting models based on mouse and keyboard features obtained the highest average F1 scores of 0.625, 0.631 and 0.775 for the multiclass prediction of perceived stress, arousal and valence, respectively. Our results indicate that the combination of mouse and keyboard features may be better suited to detect stress in office environments than heart rate variability, despite physiological signal-based stress detection being more established in theory and research. The analysis of SHAP value plots shows that specific mouse movement and typing behaviours may characterise different levels of stress., Conclusions: Our study fills different methodological gaps in the research on the automated detection of stress in office environments, such as approximating real-life conditions in a laboratory and combining physiological and behavioural data sources. Implications for field studies on personalised, interpretable ML-based systems for the real-time detection of stress in real office environments are also discussed., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: The working alliance refers to an important relationship quality between health professionals and clients that robustly links to treatment success. Recent research shows that clients can develop an affective bond with chatbots. However, few research studies have investigated whether this perceived relationship is affected by the social roles of differing closeness a chatbot can impersonate and by allowing users to choose the social role of a chatbot., Objective: This study aimed at understanding how the social role of a chatbot can be expressed using a set of interpersonal closeness cues and examining how these social roles affect clients' experiences and the development of an affective bond with the chatbot, depending on clients' characteristics (ie, age and gender) and whether they can freely choose a chatbot's social role., Methods: Informed by the social role theory and the social response theory, we developed a design codebook for chatbots with different social roles along an interpersonal closeness continuum. Based on this codebook, we manipulated a fictitious health care chatbot to impersonate one of four distinct social roles common in health care settings-institution, expert, peer, and dialogical self-and examined effects on perceived affective bond and usage intentions in a web-based lab study. The study included a total of 251 participants, whose mean age was 41.15 (SD 13.87) years; 57.0% (143/251) of the participants were female. Participants were either randomly assigned to one of the chatbot conditions (no choice: n=202, 80.5%) or could freely choose to interact with one of these chatbot personas (free choice: n=49, 19.5%). Separate multivariate analyses of variance were performed to analyze differences (1) between the chatbot personas within the no-choice group and (2) between the no-choice and the free-choice groups., Results: While the main effect of the chatbot persona on affective bond and usage intentions was insignificant (P=.87), we found differences based on participants' demographic profiles: main effects for gender (P=.04, η p 2 =0.115) and age (P<.001, η p 2 =0.192) and a significant interaction effect of persona and age (P=.01, η p 2 =0.102). Participants younger than 40 years reported higher scores for affective bond and usage intentions for the interpersonally more distant expert and institution chatbots; participants 40 years or older reported higher outcomes for the closer peer and dialogical-self chatbots. The option to freely choose a persona significantly benefited perceptions of the peer chatbot further (eg, free-choice group affective bond: mean 5.28, SD 0.89; no-choice group affective bond: mean 4.54, SD 1.10; P=.003, η p 2 =0.117)., Conclusions: Manipulating a chatbot's social role is a possible avenue for health care chatbot designers to tailor clients' chatbot experiences using user-specific demographic factors and to improve clients' perceptions and behavioral intentions toward the chatbot. Our results also emphasize the benefits of letting clients freely choose between chatbots., (©Marcia Nißen, Dominik Rüegger, Mirjam Stieger, Christoph Flückiger, Mathias Allemand, Florian v Wangenheim, Tobias Kowatsch. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.04.2022.)

Background: Health professions education has undergone major changes with the advent and adoption of digital technologies worldwide., Objective: This study aims to map the existing evidence and identify gaps and research priorities to enable robust and relevant research in digital health professions education., Methods: We searched for systematic reviews on the digital education of practicing and student health care professionals. We searched MEDLINE, Embase, Cochrane Library, Educational Research Information Center, CINAHL, and gray literature sources from January 2014 to July 2020. A total of 2 authors independently screened the studies, extracted the data, and synthesized the findings. We outlined the key characteristics of the included reviews, the quality of the evidence they synthesized, and recommendations for future research. We mapped the empirical findings and research recommendations against the newly developed conceptual framework., Results: We identified 77 eligible systematic reviews. All of them included experimental studies and evaluated the effectiveness of digital education interventions in different health care disciplines or different digital education modalities. Most reviews included studies on various digital education modalities (22/77, 29%), virtual reality (19/77, 25%), and online education (10/77, 13%). Most reviews focused on health professions education in general (36/77, 47%), surgery (13/77, 17%), and nursing (11/77, 14%). The reviews mainly assessed participants' skills (51/77, 66%) and knowledge (49/77, 64%) and included data from high-income countries (53/77, 69%). Our novel conceptual framework of digital health professions education comprises 6 key domains (context, infrastructure, education, learners, research, and quality improvement) and 16 subdomains. Finally, we identified 61 unique questions for future research in these reviews; these mapped to framework domains of education (29/61, 47% recommendations), context (17/61, 28% recommendations), infrastructure (9/61, 15% recommendations), learners (3/61, 5% recommendations), and research (3/61, 5% recommendations)., Conclusions: We identified a large number of research questions regarding digital education, which collectively reflect a diverse and comprehensive research agenda. Our conceptual framework will help educators and researchers plan, develop, and study digital education. More evidence from low- and middle-income countries is needed., (©Lorainne Tudor Car, Selina Poon, Bhone Myint Kyaw, David A Cook, Victoria Ward, Rifat Atun, Azeem Majeed, Jamie Johnston, Rianne M J J van der Kleij, Mariam Molokhia, Florian V Wangenheim, Martin Lupton, Niels Chavannes, Onyema Ajuebor, Charles G Prober, Josip Car. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.03.2022.)

Background: Advancements in technology offer new opportunities for the prevention and management of type 2 diabetes. Venture capital companies have been investing in digital diabetes companies that offer digital behavior change interventions (DBCIs). However, little is known about the scientific evidence underpinning such interventions or the degree to which these interventions leverage novel technology-driven automated developments such as conversational agents (CAs) or just-in-time adaptive intervention (JITAI) approaches., Objective: Our objectives were to identify the top-funded companies offering DBCIs for type 2 diabetes management and prevention, review the level of scientific evidence underpinning the DBCIs, identify which DBCIs are recognized as evidence-based programs by quality assurance authorities, and examine the degree to which these DBCIs include novel automated approaches such as CAs and JITAI mechanisms., Methods: A systematic search was conducted using 2 venture capital databases (Crunchbase Pro and Pitchbook) to identify the top-funded companies offering interventions for type 2 diabetes prevention and management. Scientific publications relating to the identified DBCIs were identified via PubMed, Google Scholar, and the DBCIs' websites, and data regarding intervention effectiveness were extracted. The Diabetes Prevention Recognition Program (DPRP) of the Center for Disease Control and Prevention in the United States was used to identify the recognition status. The DBCIs' publications, websites, and mobile apps were reviewed with regard to the intervention characteristics., Results: The 16 top-funded companies offering DBCIs for type 2 diabetes received a total funding of US $2.4 billion as of June 15, 2021. Only 4 out of the 50 identified publications associated with these DBCIs were fully powered randomized controlled trials (RCTs). Further, 1 of those 4 RCTs showed a significant difference in glycated hemoglobin A 1c (HbA 1c ) outcomes between the intervention and control groups. However, all the studies reported HbA 1c improvements ranging from 0.2% to 1.9% over the course of 12 months. In addition, 6 interventions were fully recognized by the DPRP to deliver evidence-based programs, and 2 interventions had a pending recognition status. Health professionals were included in the majority of DBCIs (13/16, 81%,), whereas only 10% (1/10) of accessible apps involved a CA as part of the intervention delivery. Self-reports represented most of the data sources (74/119, 62%) that could be used to tailor JITAIs., Conclusions: Our findings suggest that the level of funding received by companies offering DBCIs for type 2 diabetes prevention and management does not coincide with the level of evidence on the intervention effectiveness. There is considerable variation in the level of evidence underpinning the different DBCIs and an overall need for more rigorous effectiveness trials and transparent reporting by quality assurance authorities. Currently, very few DBCIs use automated approaches such as CAs and JITAIs, limiting the scalability and reach of these solutions., (©Roman Keller, Sven Hartmann, Gisbert Wilhelm Teepe, Kim-Morgaine Lohse, Aishah Alattas, Lorainne Tudor Car, Falk Müller-Riemenschneider, Florian von Wangenheim, Jacqueline Louise Mair, Tobias Kowatsch. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.01.2022.)

Background: Conversational agents (CAs) are a novel approach to delivering digital health interventions. In human interactions, terms of address often change depending on the context or relationship between interlocutors. In many languages, this encompasses T/V distinction -formal and informal forms of the second-person pronoun "You"-that conveys different levels of familiarity. Yet, few research articles have examined whether CAs' use of T/V distinction across language contexts affects users' evaluations of digital health applications. Methods: In an online experiment ( N = 284), we manipulated a public health CA prototype to use either informal or formal T/V distinction forms in French ("tu" vs. "vous") and German ("du" vs. "Sie") language settings. A MANCOVA and post-hoc tests were performed to examine the effects of the independent variables (i.e., T/V distinction and Language) and the moderating role of users' demographic profile (i.e., Age and Gender) on eleven user evaluation variables. These were related to four themes: (i) Sociability, (ii) CA-User Collaboration, (iii) Service Evaluation, and (iv) Behavioral Intentions. Results: Results showed a four-way interaction between T/V Distinction, Language, Age, and Gender, influencing user evaluations across all outcome themes. For French speakers, when the informal "T form" (" Tu" ) was used, higher user evaluation scores were generated for younger women and older men (e.g., the CA felt more humanlike or individuals were more likely to recommend the CA), whereas when the formal "V form" (" Vous" ) was used, higher user evaluation scores were generated for younger men and older women. For German speakers, when the informal T form (" Du" ) was used, younger users' evaluations were comparable regardless of Gender, however, as individuals' Age increased, the use of " Du" resulted in lower user evaluation scores, with this effect more pronounced in men. When using the formal V form (" Sie" ), user evaluation scores were relatively stable, regardless of Gender, and only increasing slightly with Age. Conclusions: Results highlight how user CA evaluations vary based on the T/V distinction used and language setting, however, that even within a culturally homogenous language group, evaluations vary based on user demographics, thus highlighting the importance of personalizing CA language., Competing Interests: JO, MN, and FvW are affiliated with the Center for Digital Health Interventions (www.c4dhi.org), a joint initiative of the Department of Management, Technology, and Economics at ETH Zurich and the Institute of Technology Management at the University of St. Gallen, which is funded in part by the Swiss health insurance CSS Versicherungen. CSS provided funding for recruiting participants but was neither involved in any aspect of the study design, data analysis nor manuscript preparation., (Copyright © 2022 Ollier, Nißen and von Wangenheim.)

Background: The current COVID-19 coronavirus pandemic is an emergency on a global scale, with huge swathes of the population required to remain indoors for prolonged periods to tackle the virus. In this new context, individuals' health-promoting routines are under greater strain, contributing to poorer mental and physical health. Additionally, individuals are required to keep up to date with latest health guidelines about the virus, which may be confusing in an age of social-media disinformation and shifting guidelines. To tackle these factors, we developed Elena+, a smartphone-based and conversational agent (CA) delivered pandemic lifestyle care intervention. Methods: Elena+ utilizes varied intervention components to deliver a psychoeducation-focused coaching program on the topics of: COVID-19 information, physical activity, mental health (anxiety, loneliness, mental resources), sleep and diet and nutrition. Over 43 subtopics, a CA guides individuals through content and tracks progress over time, such as changes in health outcome assessments per topic, alongside user-set behavioral intentions and user-reported actual behaviors. Ratings of the usage experience, social demographics and the user profile are also captured. Elena+ is available for public download on iOS and Android devices in English, European Spanish and Latin American Spanish with future languages and launch countries planned, and no limits on planned recruitment. Panel data methods will be used to track user progress over time in subsequent analyses. The Elena+ intervention is open-source under the Apache 2 license (MobileCoach software) and the Creative Commons 4.0 license CC BY-NC-SA (intervention logic and content), allowing future collaborations; such as cultural adaptions, integration of new sensor-related features or the development of new topics. Discussion: Digital health applications offer a low-cost and scalable route to meet challenges to public health. As Elena+ was developed by an international and interdisciplinary team in a short time frame to meet the COVID-19 pandemic, empirical data are required to discern how effective such solutions can be in meeting real world, emergent health crises. Additionally, clustering Elena+ users based on characteristics and usage behaviors could help public health practitioners understand how population-level digital health interventions can reach at-risk and sub-populations., Competing Interests: JO, PS, AA, DR, CB, OK, EF, FW, and TK are affiliated with the Centre for Digital Health Interventions www.c4dhi.org, a joint initiative of the Department of Management, Technology and Economics at ETH Zurich and the Institute of Technology Management at the University of St. Gallen, which was funded in part by the Swiss health insurer CSS Versicherung. EF and TK are also cofounders of Pathmate Technologies, a university spin-off company that creates and delivers digital clinical pathways. However, neither CSS nor Pathmate Technologies are involved in the Elena+ project or this article. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer AR declared a past co-authorship with several of the authors AS and RB to the handling editor., (Copyright © 2021 Ollier, Neff, Dworschak, Sejdiji, Santhanam, Keller, Xiao, Asisof, Rüegger, Bérubé, Hilfiker Tomas, Neff, Yao, Alattas, Varela-Mato, Pitkethly, Vara, Herrero, Baños, Parada, Agatheswaran, Villalobos, Keller, Chan, Mishra, Jacobson, Stanger, He, von Wyl, Weidt, Haug, Schaub, Kleim, Barth, Witt, Scholz, Fleisch, Wangenheim, Car, Müller-Riemenschneider, Hauser-Ulrich, Asomoza, Salamanca-Sanabria, Mair and Kowatsch.)

Background: Mental health disorders affect 1 in 10 people globally, of whom approximately 300 million are affected by depression. At least half of the people affected by depression remain untreated. Although cognitive behavioral therapy (CBT) is an effective treatment, access to mental health specialists, habitually challenging, has worsened because of the COVID-19 pandemic. Internet-based CBT is an effective and feasible strategy to increase access to treatment for people with depression. Mental health apps may further assist in facilitating self-management for people affected by depression; however, accessing the correct app may be cumbersome given the large number and wide variety of apps offered by public app marketplaces., Objective: This study aims to systematically assess the features, functionality, data security, and congruence with evidence of self-guided CBT-based apps targeting users affected by depression that are available in major app stores., Methods: We conducted a systematic assessment of self-guided CBT-based apps available in Google Play and the Apple App Store. Apps launched or updated since August 2018 were identified through a systematic search in the 42matters database using CBT-related terms. Apps meeting the inclusion criteria were downloaded and assessed using a Samsung Galaxy J7 Pro (Android 9) and iPhone 7 (iOS 13.3.1). Apps were appraised using a 182-question checklist developed by the research team, assessing their general characteristics, technical aspects and quality assurance, and CBT-related features, including 6 evidence-based CBT techniques (ie, psychoeducation, behavioral activation, cognitive restructuring, problem solving, relaxation, and exposure for comorbid anxiety) as informed by a CBT manual, CBT competence framework, and a literature review of internet-based CBT clinical trial protocols. The results were reported as a narrative review using descriptive statistics., Results: The initial search yielded 3006 apps, of which 98 met the inclusion criteria and were systematically assessed. There were 20 well-being apps; 65 mental health apps, targeting two or more common mental health disorders, including depression; and 13 depression apps. A total of 28 apps offered at least four evidence-based CBT techniques, particularly depression apps. Cognitive restructuring was the most common technique, offered by 79% (77/98) of the apps. Only one-third of the apps offered suicide risk management resources, whereas 17% (17/98) of the apps offered COVID-19-related information. Although most apps included a privacy policy, only a third of the apps presented it before account creation. In total, 82% (74/90) of privacy policies stated sharing data with third-party service providers. Half of the app development teams included academic institutions or health care providers., Conclusions: Only a few self-guided CBT-based apps offer comprehensive CBT programs or suicide risk management resources. Sharing of users' data is widespread, highlighting shortcomings in health app market governance. To fulfill their potential, self-guided CBT-based apps should follow evidence-based clinical guidelines, be patient centered, and enhance users' data security., (©Laura Martinengo, Anne-Claire Stona, Konstadina Griva, Paola Dazzan, Carmine Maria Pariante, Florian von Wangenheim, Josip Car. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.07.2021.)

Background: Chronic and mental health conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention and management of these conditions in a scalable manner. However, evidence on VCAs dedicated to the prevention and management of chronic and mental health conditions is unclear., Objective: This study provides a better understanding of the current methods used in the evaluation of health interventions for the prevention and management of chronic and mental health conditions delivered through VCAs., Methods: We conducted a systematic literature review using PubMed MEDLINE, Embase, PsycINFO, Scopus, and Web of Science databases. We included primary research involving the prevention or management of chronic or mental health conditions through a VCA and reporting an empirical evaluation of the system either in terms of system accuracy, technology acceptance, or both. A total of 2 independent reviewers conducted the screening and data extraction, and agreement between them was measured using Cohen kappa. A narrative approach was used to synthesize the selected records., Results: Of 7170 prescreened papers, 12 met the inclusion criteria. All studies were nonexperimental. The VCAs provided behavioral support (n=5), health monitoring services (n=3), or both (n=4). The interventions were delivered via smartphones (n=5), tablets (n=2), or smart speakers (n=3). In 2 cases, no device was specified. A total of 3 VCAs targeted cancer, whereas 2 VCAs targeted diabetes and heart failure. The other VCAs targeted hearing impairment, asthma, Parkinson disease, dementia, autism, intellectual disability, and depression. The majority of the studies (n=7) assessed technology acceptance, but only few studies (n=3) used validated instruments. Half of the studies (n=6) reported either performance measures on speech recognition or on the ability of VCAs to respond to health-related queries. Only a minority of the studies (n=2) reported behavioral measures or a measure of attitudes toward intervention-targeted health behavior. Moreover, only a minority of studies (n=4) reported controlling for participants' previous experience with technology. Finally, risk bias varied markedly., Conclusions: The heterogeneity in the methods, the limited number of studies identified, and the high risk of bias show that research on VCAs for chronic and mental health conditions is still in its infancy. Although the results of system accuracy and technology acceptance are encouraging, there is still a need to establish more conclusive evidence on the efficacy of VCAs for the prevention and management of chronic and mental health conditions, both in absolute terms and in comparison with standard health care., (©Caterina Bérubé, Theresa Schachner, Roman Keller, Elgar Fleisch, Florian v Wangenheim, Filipe Barata, Tobias Kowatsch. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.03.2021.)

Background: Successful management of chronic diseases requires a trustful collaboration between health care professionals, patients, and family members. Scalable conversational agents, designed to assist health care professionals, may play a significant role in supporting this collaboration in a scalable way by reaching out to the everyday lives of patients and their family members. However, to date, it remains unclear whether conversational agents, in such a role, would be accepted and whether they can support this multistakeholder collaboration., Objective: With asthma in children representing a relevant target of chronic disease management, this study had the following objectives: (1) to describe the design of MAX, a conversational agent-delivered asthma intervention that supports health care professionals targeting child-parent teams in their everyday lives; and (2) to assess the (a) reach of MAX, (b) conversational agent-patient working alliance, (c) acceptance of MAX, (d) intervention completion rate, (e) cognitive and behavioral outcomes, and (f) human effort and responsiveness of health care professionals in primary and secondary care settings., Methods: MAX was designed to increase cognitive skills (ie, knowledge about asthma) and behavioral skills (ie, inhalation technique) in 10-15-year-olds with asthma, and enables support by a health professional and a family member. To this end, three design goals guided the development: (1) to build a conversational agent-patient working alliance; (2) to offer hybrid (human- and conversational agent-supported) ubiquitous coaching; and (3) to provide an intervention with high experiential value. An interdisciplinary team of computer scientists, asthma experts, and young patients with their parents developed the intervention collaboratively. The conversational agent communicates with health care professionals via email, with patients via a mobile chat app, and with a family member via SMS text messaging. A single-arm feasibility study in primary and secondary care settings was performed to assess MAX., Results: Results indicated an overall positive evaluation of MAX with respect to its reach (49.5%, 49/99 of recruited and eligible patient-family member teams participated), a strong patient-conversational agent working alliance, and high acceptance by all relevant stakeholders. Moreover, MAX led to improved cognitive and behavioral skills and an intervention completion rate of 75.5%. Family members supported the patients in 269 out of 275 (97.8%) coaching sessions. Most of the conversational turns (99.5%) were conducted between patients and the conversational agent as opposed to between patients and health care professionals, thus indicating the scalability of MAX. In addition, it took health care professionals less than 4 minutes to assess the inhalation technique and 3 days to deliver related feedback to the patients. Several suggestions for improvement were made., Conclusions: This study provides the first evidence that conversational agents, designed as mediating social actors involving health care professionals, patients, and family members, are not only accepted in such a "team player" role but also show potential to improve health-relevant outcomes in chronic disease management., (©Tobias Kowatsch, Theresa Schachner, Samira Harperink, Filipe Barata, Ullrich Dittler, Grace Xiao, Catherine Stanger, Florian v Wangenheim, Elgar Fleisch, Helmut Oswald, Alexander Möller. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.02.2021.)

Background: Recent years have witnessed a constant increase in the number of people with chronic conditions requiring ongoing medical support in their everyday lives. However, global health systems are not adequately equipped for this extraordinarily time-consuming and cost-intensive development. Here, conversational agents (CAs) can offer easily scalable and ubiquitous support. Moreover, different aspects of CAs have not yet been sufficiently investigated to fully exploit their potential. One such trait is the interaction style between patients and CAs. In human-to-human settings, the interaction style is an imperative part of the interaction between patients and physicians. Patient-physician interaction is recognized as a critical success factor for patient satisfaction, treatment adherence, and subsequent treatment outcomes. However, so far, it remains effectively unknown how different interaction styles can be implemented into CA interactions and whether these styles are recognizable by users., Objective: The objective of this study was to develop an approach to reproducibly induce 2 specific interaction styles into CA-patient dialogs and subsequently test and validate them in a chronic health care context., Methods: On the basis of the Roter Interaction Analysis System and iterative evaluations by scientific experts and medical health care professionals, we identified 10 communication components that characterize the 2 developed interaction styles: deliberative and paternalistic interaction styles. These communication components were used to develop 2 CA variations, each representing one of the 2 interaction styles. We assessed them in a web-based between-subject experiment. The participants were asked to put themselves in the position of a patient with chronic obstructive pulmonary disease. These participants were randomly assigned to interact with one of the 2 CAs and subsequently asked to identify the respective interaction style. Chi-square test was used to assess the correct identification of the CA-patient interaction style., Results: A total of 88 individuals (42/88, 48% female; mean age 31.5 years, SD 10.1 years) fulfilled the inclusion criteria and participated in the web-based experiment. The participants in both the paternalistic and deliberative conditions correctly identified the underlying interaction styles of the CAs in more than 80% of the assessments (X 2 1,88 =38.2; P<.001; phi coefficient r φ =0.68). The validation of the procedure was hence successful., Conclusions: We developed an approach that is tailored for a medical context to induce a paternalistic and deliberative interaction style into a written interaction between a patient and a CA. We successfully tested and validated the procedure in a web-based experiment involving 88 participants. Future research should implement and test this approach among actual patients with chronic diseases and compare the results in different medical conditions. This approach can further be used as a starting point to develop dynamic CAs that adapt their interaction styles to their users., (©Theresa Schachner, Christoph Gross, Andrea Hasl, Florian v Wangenheim, Tobias Kowatsch. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.01.2021.)

Background: The high prevalence of office stress and its detrimental health consequences are of concern to individuals, employers and society at large. Laboratory studies investigating office stress have mostly relied on data from participants that were tested individually on abstract tasks. In this study, we examined the effect of psychosocial office stress and work interruptions on the psychobiological stress response in a realistic but controlled group office environment. We also explored the role of cognitive stress appraisal as an underlying mechanism mediating the relationship between work stressors and the stress response., Methods and Materials: Ninety participants (44 female; mean age 23.11 ± 3.80) were randomly assigned to either a control condition or one of two experimental conditions in which they were exposed to psychosocial stress with or without prior work interruptions in a realistic multi-participant laboratory setting. To induce psychosocial stress, we adapted the Trier Social Stress Test for Groups to an office environment. Throughout the experiment, we continuously monitored heart rate and heart rate variability. Participants repeatedly reported on their current mood, calmness, wakefulness and perceived stress and gave saliva samples to assess changes in salivary cortisol and salivary alpha-amylase. Additionally, cognitive appraisal of the psychosocial stress test was evaluated., Results: Our analyses revealed significant group differences for most outcomes during or immediately after the stress test (i.e., mood, calmness, perceived stress, salivary cortisol, heart rate, heart rate variability) and during recovery (i.e., salivary cortisol and heart rate). Interestingly, the condition that experienced work interruptions showed a higher increase of cortisol levels but appraised the stress test as less threatening than individuals that experienced only psychosocial stress. Exploratory mediation analyses revealed a blunted response in subjective measures of stress, which was partially explained by the differences in threat appraisal., Discussion: The results showed that experimentally induced work stress led to significant responses of subjective measures of stress, the hypothalamic-pituitary-adrenal axis and the autonomic nervous system. However, there appears to be a discrepancy between the psychological and biological responses to preceding work interruptions. Appraising psychosocial stress as less threatening but still as challenging could be an adaptive way of coping and reflect a state of engagement and eustress., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Chronic obstructive pulmonary disease (COPD) is one of the most common disorders in the world. COPD is characterized by airflow obstruction, which is not fully reversible. Patients usually experience breathing-related symptoms with periods of acute worsening and a substantial decrease in the health-related quality-of-life. Active and comprehensive disease management can slow down the progressive course of the disease and improve patients' disabilities. Technological progress and digitalization of medicine have the potential to make elaborate interventions easily accessible and applicable to a broad spectrum of patients with COPD without increasing the costs of the intervention., Objective: This study aims to develop a comprehensive telemonitoring and hybrid virtual coaching solution and to investigate its effects on the health-related quality of life of patients with COPD., Methods: A monocentric, assessor-blind, two-arm (intervention/control) randomized controlled trial will be performed. Participants randomized to the control group will receive usual care and a CAir Desk (custom-built home disease-monitoring device to telemonitor disease-relevant parameters) for 12 weeks, without feedback or scores of the telemonitoring efforts and virtual coaching. Participants randomized to the intervention group will receive a CAir Desk and a hybrid digital coaching intervention for 12 weeks. As a primary outcome, we will measure the delta in the health-related quality of life, which we will assess with the St. George Respiratory Questionnaire, from baseline to week 12 (the end of the intervention)., Results: The development of the CAir Desk and virtual coach has been completed. Recruitment to the trial started in September 2020. We expect to start data collection by December 2020 and expect it to last for approximately 18 months, as we follow a multiwave approach. We expect to complete data collection by mid-2022 and plan the dissemination of the results subsequently., Conclusions: To our knowledge, this is the first study investigating a combination of telemonitoring and hybrid virtual coaching in patients with COPD. We will investigate the effectiveness, efficacy, and usability of the proposed intervention and provide evidence to further develop app-based and chatbot-based disease monitoring and interventions in COPD., Trial Registration: ClinicalTrials.gov identifier: NCT04373070; https://clinicaltrials.gov/ct2/show/NCT04373070., International Registered Report Identifier (irrid): DERR1-10.2196/20412., (©Christoph Gross, Dario Kohlbrenner, Christian F Clarenbach, Adam Ivankay, Thomas Brunschwiler, Yves Nordmann, Florian v Wangenheim. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 22.10.2020.)

Background: A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients., Objective: The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases., Methods: We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms "conversational agent," "healthcare," "artificial intelligence," and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis., Results: The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results., Conclusions: The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes., (©Theresa Schachner, Roman Keller, Florian von Wangenheim. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 14.09.2020.)