Embodied Conversational Agents in Clinical Psychology: A Scoping Review

A Novel Virtual Coaching System Based on Personalized Clinical Pathways for Rehabilitation of Older Adults-Requirements and Implementation Plan of the vCare Project

Home-based rehabilitation after an acute episode or following an exacerbation of a chronic disease is often problematic with a clear lack of continuity of care between hospital and home care. Secondary prevention is an essential element of long-term rehabilitation where strategies oriented toward risk reduction, treatment adherence, and optimization of quality of life need to be applied. Frail and sometimes isolated, the patient fails to adhere to the proposed post-discharge clinical pathway due to lack of appropriate clinical, emotional, and informational support. Providing a suitable rehabilitation after an acute episode or a chronic disease is a major issue, as it helps people to live independently and enhance their quality of life. However, as the rehabilitation period usually lasts some months, the continuity of care is often interrupted in the transition from hospital to home. Virtual coaches could help these patients to engage in a personalized rehabilitation program that complies with age-related conditions. These coaches could be a key technology for empowering patients toward increasing their adherence to the care plan and to improve their secondary prevention measures. In this paper, we are presenting a novel virtual coaching system that will address these challenges by combining recent technological advances with clinical pathways, based on joint research and validation activities from researchers from the medical and information and communication technology (ICT) domains.

A Review of Virtual Coaching Systems in Healthcare: Closing the Loop With Real-Time Feedback

This review focuses on virtual coaching systems that were designed to enhance healthcare interventions, combining the available sensing and system-user interaction technologies. In total, more than 1,200 research papers have been retrieved and evaluated for the purposes of this review, which were obtained from three online databases (i.e.,PubMed, Scopus and IEEE Xplore) using an extensive set of search keywords. After applying exclusion criteria, the remaining 41 research papers were used to evaluate the status of virtual coaching systems over the past 10 years and assess current and future trends in this field. The results suggest that in home coaching systems were mainly focused in promoting physical activity and a healthier lifestyle, while a wider range of medical domains was considered in systems that were evaluated in lab environment. In home patient monitoring with IoT devices and sensors was mostly limited to activity trackers, pedometers and heart rate monitoring. Real-time evaluations and personalized patient feedback was also found to be rather lacking in home coaching systems and this is the most alarming find of this analysis. Feasibility studies in controlled environment and an ongoing active research on Horizon 2020 funded projects, show that the future trends in this field are aiming to close the loop with automated patient monitoring, real-time evaluations and more precise interventions.

Elena+ Care for COVID-19, a Pandemic Lifestyle Care Intervention: Intervention Design and Study Protocol

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.

Use and effect of embodied conversational agents for improving eating behavior and decreasing loneliness among community-dwelling older adults: A randomized controlled trial (Preprint)

Background

Embodied conversational agents (ECAs) have been proposed as a promising interaction modality for the delivery of programs focused on promoting lifestyle changes. However, it is not understood what factors influence the health effects of ECAs or their use.

Objective

We aimed to (1) identify whether ECAs could persuade community-dwelling older adults to change their dietary behavior and whether ECA use could decrease loneliness, (2) test the pathways to these effects, and (3) understand factors influencing the use of ECAs.

Methods

A randomized controlled trial was conducted. The intervention group received access to the PACO service for 8 weeks. The waitlist group started PACO use after waiting for 4 weeks. Two primary outcomes (eating behavior and loneliness) were assessed via online questionnaires at intake, upon joining the waitlist, after 4 weeks, and after 8 weeks. The third primary outcome (use) was assessed via data logs. Secondary outcomes were measured at the same time points, via questionnaires or an optional interview.

Results

In total, 32 participants completed the intervention. We found a significant correlation between use in minutes on the one hand, and perceived usefulness (r=0.39, P=.03) and enjoyment on the other (r=0.38, P=.03). However, these did not predict use in the full regression model (F_2,29=1.98, P=.16, R²=0.12). Additionally, PACO use did not lead to improvement in eating behavior (χ²₂=0.34, P=.85) or a decrease in loneliness (χ²₂=0.02, P=.99).

Conclusions

Our study did not provide any concluding evidence about factors that are linked to the use or health effects of ECAs. Future service design could benefit from either creating a functional design catering to the predominant stage in the precaution adoption process model of the targeted population, or by personalizing the service based on an intake in which the end user’s stage is determined.

Trial Registration

ClinicalTrials.gov NCT04510883; https://clinicaltrials.gov/ct2/show/NCT04510883

International Registered Report Identifier (IRRID)

RR2-10.2196/22186

Toward the Integration of Technology-Based Interventions in the Care Pathway for People with Dementia: A Cross-National Study

BACKGROUND

The integration of technology-based interventions into health and care provision in our aging society is still a challenge especially in the care pathway for people with dementia.

OBJECTIVE

The study aims to: (1) identify which socio-demographic characteristics are independently associated with the use of the embodied conversational agent among subjects with dementia, (2) uncover patient cluster profiles based on these characteristics, and (3) discuss technology-based interventions challenges.

METHODS

A virtual agent was used for four weeks by 55 persons with dementia living in their home environment.

RESULTS

Participants evaluated the agent as easy-to-use and quickly learnable. They felt confident while using the system and expressed the willingness to use it frequently. Moreover, 21/55 of the patients perceived the virtual agent as a friend and assistant who they could feel close to and who would remind them of important things.

CONCLUSIONS

Technology-based interventions require a significant effort, such as personalized features and patient-centered care pathways, to be effective. Therefore, this study enriches the open discussion on how such virtual agents must be evidence-based related and designed by multidisciplinary teams, following patient-centered care as well as user-centered design approaches.

Development, Feasibility, Acceptability, and Utility of an Expressive Speech-Enabled Digital Health Agent to Deliver Online, Brief Motivational Interviewing for Alcohol Misuse: Descriptive Study

Background

Digital health agents — embodied conversational agents designed specifically for health interventions — provide a promising alternative or supplement to behavioral health services by reducing barriers to access to care.

Objective

Our goals were to (1) develop an expressive, speech-enabled digital health agent operating in a 3-dimensional virtual environment to deliver a brief behavioral health intervention over the internet to reduce alcohol use and to (2) understand its acceptability, feasibility, and utility with its end users.

Methods

We developed an expressive, speech-enabled digital health agent with facial expressions and body gestures operating in a 3-dimensional virtual office and able to deliver a brief behavioral health intervention over the internet to reduce alcohol use. We then asked 51 alcohol users to report on the digital health agent acceptability, feasibility, and utility.

Results

The developed digital health agent uses speech recognition and a model of empathetic verbal and nonverbal behaviors to engage the user, and its performance enabled it to successfully deliver a brief behavioral health intervention over the internet to reduce alcohol use. Descriptive statistics indicated that participants had overwhelmingly positive experiences with the digital health agent, including engagement with the technology, acceptance, perceived utility, and intent to use the technology. Illustrative qualitative quotes provided further insight about the potential reach and impact of digital health agents in behavioral health care.

Conclusions

Web-delivered interventions delivered by expressive, speech-enabled digital health agents may provide an exciting complement or alternative to traditional one-on-one treatment. They may be especially helpful for hard-to-reach communities with behavioral workforce shortages.

Perspectives fondamentale, clinique et sociétale de l’utilisation des personnages virtuels en santé mentale

Avec l’attrait engendré par les avancées en informatique et en intelligence artificielle, les personnages virtuels (c.-à-d. personnages représentés numériquement d’apparence humaine ou non) sont pressentis comme de futurs prestataires de soins en santé mentale. À ce jour, l’utilisation principale de tels personnages est toutefois marginale et se limite à une aide complémentaire à la pratique des cliniciens. Des préoccupations liées à la sécurité et l’efficacité, ainsi qu’un manque de connaissances et de compétences peuvent expliquer cette discordance entre ce que certains s’imaginent être l’utilisation future (voire futuriste) des personnages virtuels et leur utilisation actuelle. Un aperçu des récentes données probantes contribuerait à réduire cette divergence et à mieux saisir les enjeux associés à leur utilisation plus répandue en santé mentale.

Objectif Cet article vise à informer tous les acteurs impliqués, dont les cliniciens, quant au potentiel des personnages virtuels en santé mentale, et de les sensibiliser aux enjeux associés à leur usage.

Méthode Une recension narrative de la littérature a été réalisée afin de synthétiser les informations obtenues de la recherche fondamentale et clinique, et de discuter des considérations sociétales.

Résultats Plusieurs caractéristiques des personnages virtuels provenant de la recherche fondamentale ont le potentiel d’influencer les interactions entre un patient et un clinicien. Elles peuvent être regroupées en deux grandes catégories : les caractéristiques liées à la perception (p. ex. réalisme) et celles liées à l’attribution spontanée d’une catégorie sociale au personnage virtuel par un observateur (p. ex. genre). Selon la recherche clinique, plusieurs interventions ou évaluations utilisant des personnages virtuels ont montré divers degrés d’efficacité en santé mentale, et certains éléments de la relation thérapeutique (p. ex. alliance et empathie) peuvent d’ailleurs être présents lors d’une relation avec un personnage virtuel. De multiples enjeux socioéconomiques et éthiques doivent aussi être discutés en vue d’un développement et d’une utilisation plus accrue qui soient responsables et éthiques. Bien que l’accessibilité et la disponibilité des personnages virtuels constituent un avantage indéniable pour l’offre de services en santé mentale, certaines iniquités demeurent. L’accumulation de données biométriques (p. ex. rythme cardiaque) a également le potentiel d’enrichir le travail des cliniciens, mais aussi de mener au développement de personnages virtuels autonomes à l’aide de l’intelligence artificielle, ce qui pourrait conduire à certains dérapages (p. ex. erreurs de décision clinique). Quelques pistes de recommandations visant à éviter ces effets indésirables sont présentées.

Conclusion L’emploi des personnages virtuels sera de plus en plus répandu en santé mentale en raison de leurs avantages prometteurs. Ainsi, il est souhaitable que tous les acteurs impliqués s’informent sur leur usage dans ce contexte, se sensibilisent aux enjeux spécifiques, participent activement aux discussions quant à leur développement et adoptent des recommandations uniformes en vue d’un usage sécuritaire et éthique en santé mentale.

Chatbots to Support Mental Wellbeing of People Living in Rural Areas: Can User Groups Contribute to Co-design?

Digital technologies such as chatbots can be used in the field of mental health. In particular, chatbots can be used to support citizens living in sparsely populated areas who face problems such as poor access to mental health services, lack of 24/7 support, barriers to engagement, lack of age appropriate support and reductions in health budgets. The aim of this study was to establish if user groups can design content for a chatbot to support the mental wellbeing of individuals in rural areas. University students and staff, mental health professionals and mental health service users (N = 78 total) were recruited to workshops across Northern Ireland, Ireland, Scotland, Finland and Sweden. The findings revealed that participants wanted a positive chatbot that was able to listen, support, inform and build a rapport with users. Gamification could be used within the chatbot to increase user engagement and retention. Content within the chatbot could include validated mental health scales and appropriate response triggers, such as signposting to external resources should the user disclose potentially harmful information or suicidal intent. Overall, the workshop participants identified user needs which can be transformed into chatbot requirements. Responsible design of mental healthcare chatbots should consider what users want or need, but also what chatbot features artificial intelligence can competently facilitate and which features mental health professionals would endorse.

In Search of Embodied Conversational and Explainable Agents for Health Behaviour Change and Adherence

Conversational agents offer promise to provide an alternative to costly and scarce access to human health providers. Particularly in the context of adherence to treatment advice and health behavior change, they can provide an ongoing coaching role to motivate and keep the health consumer on track. Due to the recognized importance of face-to-face communication and establishment of a therapist-patient working alliance as the biggest single predictor of adherence, our review focuses on embodied conversational agents (ECAs) and their use in health and well-being interventions. The article also introduces ECAs who provide explanations of their recommendations, known as explainable agents (XAs), as a way to build trust and enhance the working alliance towards improved behavior change. Of particular promise, is work in which XAs are able to engage in conversation to learn about their user and personalize their recommendations based on their knowledge of the user and then tailor their explanations to the beliefs and goals of the user to increase relevancy and motivation and address possible barriers to increase intention to perform the healthy behavior.

Conversational Agents for Health and Well-being Across the Life Course: Protocol for an Evidence Map

BACKGROUND

Conversational agents, which we defined as computer programs that are designed to simulate two-way human conversation by using language and are potentially supplemented with nonlanguage modalities, offer promising avenues for health interventions for different populations across the life course. There is a lack of open-access and user-friendly resources for identifying research trends and gaps and pinpointing expertise across international centers.

OBJECTIVE

Our aim is to provide an overview of all relevant evidence on conversational agents for health and well-being across the life course. Specifically, our objectives are to identify, categorize, and synthesize-through visual formats and a searchable database-primary studies and reviews in this research field.

METHODS

An evidence map was selected as the type of literature review to be conducted, as it optimally corresponded to our aim. We systematically searched 8 databases (MEDLINE; CINAHL; Web of Science; Scopus; the Cochrane, ACM, IEEE, and Joanna Briggs Institute databases; and Google Scholar). We will perform backward citation searching on all included studies. The first stage of a double-stage screening procedure, which was based on abstracts and titles only, was conducted by using predetermined eligibility criteria for primary studies and reviews. An operational screening procedure was developed for streamlined and consistent screening across the team. Double data extraction will be performed with previously piloted data collection forms. We will appraise systematic reviews by using A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2. Primary studies and reviews will be assessed separately in the analysis. Data will be synthesized through descriptive statistics, bivariate statistics, and subgroup analysis (if appropriate) and through high-level maps such as scatter and bubble charts. The development of the searchable database will be informed by the research questions and data extraction forms.

RESULTS

As of April 2021, the literature search in the eight databases was concluded, yielding a total of 16,351 records. The first stage of screening, which was based on abstracts and titles only, resulted in the selection of 1282 records of primary studies and 151 records of reviews. These will be subjected to second-stage screening. A glossary with operational definitions for supporting the study selection and data extraction stages was drafted. The anticipated completion date is October 2021.

CONCLUSIONS

Our wider definition of a conversational agent and the broad scope of our evidence map will explicate trends and gaps in this field of research. Additionally, our evidence map and searchable database of studies will help researchers to avoid fragmented research efforts and wasteful redundancies. Finally, as part of the Harnessing the Power of Conversational e-Coaches for Health and Well-being Through Swiss-Portuguese Collaboration project, our work will also inform the development of an international taxonomy on conversational agents for health and well-being, thereby contributing to terminology standardization and categorization.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/26680.

User Requirements Analysis of an Embodied Conversational Agent for Coaching Older Adults to Choose Active and Healthy Ageing Behaviors during the Transition to Retirement: A Cross-National User Centered Design Study

Background: Retirement is recognized as a factor influencing the ageing process. Today, virtual health coaching systems can play a pivotal role in supporting older adults’ active and healthy ageing. This study wants to answer two research questions: (1) What are the user requirements of a virtual coach (VC) based on an Embodied Conversational Agent (ECA) for motivating older adults in transition to retirement to adopt a healthy lifestyle? (2) How could a VC address the active and healthy ageing dimensions, even during COVID-19 times? Methods: Two-wave focus-groups with 60 end-users aged 55 and over and 27 follow-up telephone interviews were carried out in Austria, Italy and the Netherlands in 2019–2020. Qualitative data were analysed by way of framework analysis. Results: End-users suggest the VC should motivate older workers and retirees to practice physical activity, maintain social contacts and emotional well-being. The ECA should be reactive, customizable, expressive, sympathetic, not directive nor patronizing, with a pleasant and motivating language. The COVID-19 outbreak increased the users’ need for functions boosting community relationships and promoting emotional well-being. Conclusions: the VC can address the active and healthy ageing paradigm by increasing the chances of doing low-cost healthy activities at any time and in any place.

E-Health Practices and Technologies: A Systematic Review from 2014 to 2019

E-health can be defined as a set of technologies applied with the help of the internet, in which healthcare services are provided to improve quality of life and facilitate healthcare delivery. As there is a lack of similar studies on the topic, this analysis uses a systematic literature review of articles published from 2014 to 2019 to identify the most common e-health practices used worldwide, as well as the main services provided, diseases treated, and the associated technologies that assist in e-health practices. Some of the key results were the identification of the four most common practices used (mhealth or mobile health; telehealth or telemedicine; technology; and others) and the most widely used technologies associated with e-health (IoT, cloud computing, Big Data, security, and systems).

A scoping review of patient-facing, behavioral health interventions with voice assistant technology targeting self-management and healthy lifestyle behaviors

Engaging in positive healthy lifestyle behaviors continues to be a public health challenge, requiring innovative solutions. As the market for voice assistants (Amazon Alexa, Google Assistant, and Apple Siri) grows and people increasingly use them to assist their daily tasks, there is a pressing need to explore how voice assistant (VA) technology may be used in behavioral health interventions. A scoping review of literature was conducted to address a PICO (Population, Intervention, Comparison, and Outcome) question: across populations, how does the use of voice assistants in behavioral health research/interventions influence healthy lifestyle behaviors versus control or comparison interventions? To inform the science, a secondary aim of this review was to explore characteristics of VAs used in behavioral health research. The review was conducted following Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines with scoping review extension (PRISMA-ScR). Ten studies satisfied the inclusion criteria, representing research published through February 2019. Studies spanned pediatric to elderly populations, covering a vast array of self-management and healthy lifestyle behaviors. The majority of interventions were multicomponent, involving more than one of the following behavior change techniques grouped by cluster: shaping knowledge, self-belief, repetition and substitution, feedback and monitoring, goals and planning, antecedents, natural consequences, comparison of behavior, and identification. However, most studies were in early stages of development, with limited efficacy trials. VA technology continues to evolve and support behavioral interventions using various platforms (e.g., Interactive Voice Response [IVR] systems, smartphones, and smart speakers) which are used alone or in conjunction with other platforms. Feasibility, usability, preliminary efficacy, along with high user satisfaction of research adapted VAs, in contrast to standalone commercially available VAs, suggest a role for VAs in behavioral health intervention research.

The professional representation of conversational agents for health care: a scoping review protocol

OBJECTIVE

The purpose of this scoping review is to examine the professional representation of conversational agents that are used for health care. Professional characteristics associated with these agents will be identified, and the prevalence of these characteristics will be determined.

INTRODUCTION

Conversational agents that are used for health care lack the qualifications and capabilities of real health professionals, but this fact may not be clear to some patients and health seekers. This problem may be exacerbated when conversational agents are described as health professionals or are given professional titles or appearances. To date, the professional representation of conversational agents that are used for health care has received little attention in the literature.

INCLUSION CRITERIA

This review will include scholarly publications on conversational agents that are used for health care, particularly descriptive/developmental case studies and intervention/evaluation studies. This review will consider conversational agents designed for patients and health seekers, but not health professionals or trainees. Agents addressing physical and/or mental health will be considered.

METHODS

This review will be conducted in accordance with JBI methodology for scoping reviews. The databases to be searched will include MEDLINE (PubMed), Embase (Elsevier), CINAHL with Full Text (EBSCO), Scopus (Elsevier), Web of Science (Clarivate), ACM Guide to Computing Literature (ACM Digital Library), and IEEE Xplore (IEEE). The extracted data will include study characteristics, basic characteristics of the conversational agent, and characteristics relating to the professional representation of the conversational agent. The extracted data will be presented in tabular form and summarized using frequency analysis. These results will be accompanied by a narrative summary.

Is the Automation of Digital Mental Health Ethical? Applying an Ethical Framework to Chatbots for Cognitive Behaviour Therapy

The COVID-19 pandemic has intensified the need for mental health support across the whole spectrum of the population. Where global demand outweighs the supply of mental health services, established interventions such as cognitive behavioural therapy (CBT) have been adapted from traditional face-to-face interaction to technology-assisted formats. One such notable development is the emergence of Artificially Intelligent (AI) conversational agents for psychotherapy. Pre-pandemic, these adaptations had demonstrated some positive results; but they also generated debate due to a number of ethical and societal challenges. This article commences with a critical overview of both positive and negative aspects concerning the role of AI-CBT in its present form. Thereafter, an ethical framework is applied with reference to the themes of (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. These themes are then discussed in terms of practical recommendations for future developments. Although automated versions of therapeutic support may be of appeal during times of global crises, ethical thinking should be at the core of AI-CBT design, in addition to guiding research, policy, and real-world implementation as the world considers post-COVID-19 society.

The Use and Promise of Conversational Agents in Digital Health

OBJECTIVES

To describe the use and promise of conversational agents in digital health-including health promotion andprevention-and how they can be combined with other new technologies to provide healthcare at home.

METHOD

A narrative review of recent advances in technologies underpinning conversational agents and their use and potential for healthcare and improving health outcomes.

RESULTS

By responding to written and spoken language, conversational agents present a versatile, natural user interface and have the potential to make their services and applications more widely accessible. Historically, conversational interfaces for health applications have focused mainly on mental health, but with an increase in affordable devices and the modernization of health services, conversational agents are becoming more widely deployed across the health system. We present our work on context-aware voice assistants capable of proactively engaging users and delivering health information and services. The proactive voice agents we deploy, allow us to conduct experience sampling in people's homes and to collect information about the contexts in which users are interacting with them.

CONCLUSION

In this article, we describe the state-of-the-art of these and other enabling technologies for speech and conversation and discuss ongoing research efforts to develop conversational agents that "live" with patients and customize their service offerings around their needs. These agents can function as 'digital companions' who will send reminders about medications and appointments, proactively check in to gather self-assessments, and follow up with patients on their treatment plans. Together with an unobtrusive and continuous collection of other health data, conversational agents can provide novel and deeply personalized access to digital health care, and they will continue to become an increasingly important part of the ecosystem for future healthcare delivery.

Voice-enabled Intelligent Virtual Agents for People with Amnesia: Systematic Review (Preprint)

Background

Objective

Methods

Results

Conclusions

Choice of Behavioral Change Techniques in Health Care Conversational Agents: Protocol for a Scoping Review

Background

Conversational agents or chatbots are computer programs that simulate conversations with users. Conversational agents are increasingly used for delivery of behavior change interventions in health care. Behavior change is complex and comprises the use of one or several components collectively known as behavioral change techniques (BCTs).

Objective

The objective of this scoping review is to identify the BCTs that are used in behavior change–focused interventions delivered via conversational agents in health care.

Methods

This scoping review will be performed in line with the Joanna Briggs Institute methodology and will be reported according to the PRISMA extension for scoping reviews guidelines. We will perform a comprehensive search of electronic databases and grey literature sources, and will check the reference lists of included studies for additional relevant studies. The screening and data extraction will be performed independently and in parallel by two review authors. Discrepancies will be resolved through consensus or discussion with a third review author. We will use a data extraction form congruent with the key themes and aims of this scoping review. BCTs employed in the included studies will be coded in line with BCT Taxonomy v1. We will analyze the data qualitatively and present it in diagrammatic or tabular form, alongside a narrative summary.

Results

To date, we have designed the search strategy and performed the search on April 26, 2021. The first round of screening of retrieved articles is planned to begin soon.

Conclusions

Using appropriate BCTs in the design and delivery of health care interventions via conversational agents is essential to improve long-term outcomes. Our findings will serve to inform the development of future interventions in this area.

International Registered Report Identifier (IRRID)

PRR1-10.2196/30166

Embodied Conversational Agents for Patients With Dementia: Thematic Literature Analysis

Background

As the world’s population rapidly ages, the number of older adults with cognitive impairment will also increase. Several studies have identified numerous complex needs of people with dementia, which assistive technologies still fail to support. Recent trends have led to an increasing focus on the use of embodied conversational agents (ECAs) as virtual entities able to interact with a person through natural and familiar verbal and nonverbal communication. The use of ECAs could improve the accessibility and acceptance of assistive technologies matching those high-level needs that are not well covered to date.

Objective

The aim of this thematic literature analysis was to map current studies in the field of designing ECAs for patients with dementia in order to identify the existing research trend and possible gaps that need to be covered in the near future. The review questions in this study were as follows: (1) what research frameworks are used to study the interaction between patients with dementia and ECAs? (2) what are the findings? and (3) what are the barriers reported in these studies?

Methods

Separate literature searches were conducted in PubMed, Web of Science, Scopus, and Embase databases by using specific umbrella phrases to target the population (patients with dementia) and the technology-based intervention (embodied conversational agent). Studies that met the inclusion criteria were appraised through the Mixed Methods Appraisal Tool and then discussed in a thematic analysis.

Results

The search process identified 115 records from the databases and study references. After duplicates (n=45) were removed, 70 papers remained for the initial screening. A total of 7 studies were finally included in the qualitative synthesis. A thematic analysis of the reviewed studies identified major themes and subthemes: the research frameworks used to gather users’ perspectives on ECAs (theme 1), the insights shared by the 7 studies as well as the value of user involvement in the development phases and the challenge of matching the system functionalities with the users’ needs (theme 2), and the main methodological and technical problems faced by each study team (theme 3).

Conclusions

Our thematic literature analysis shows that the field of ECAs is novel and poorly discussed in the scientific community and that more sophisticated study designs and proofs of efficacy of the approach are required. Therefore, by analyzing the main topic of the narrative review, this study underscores the challenge of synchronizing and harmonizing knowledge, efforts, and challenges in the dementia care field and its person-centered paradigm through the user-centered design approach. Enabling strict collaboration between interdisciplinary research networks, medical scientists, technology developers, patients, and their formal and informal caregivers is still a great challenge in the field of technologies for older adults.

Multilingual Conversational Systems to Drive the Collection of Patient-Reported Outcomes and Integration into Clinical Workflows

Patient-reported outcomes (PROs) and their use in the clinical workflow can improve cancer survivors’ outcomes and quality of life. However, there are several challenges regarding efficient collection of the patient-reported outcomes and their integration into the clinical workflow. Patient adherence and interoperability are recognized as main barriers. This work implements a cancer-related study which interconnects artificial intelligence (spoken language algorithms, conversational intelligence) and natural sciences (embodied conversational agents) to create an omni-comprehensive system enabling symmetric computer-mediated interaction. Its goal is to collect patient information and integrate it into clinical routine as digital patient resources (the Fast Healthcare Interoperability Resources). To further increase convenience and simplicity of the data collection, a multimodal sensing network is delivered. In this paper, we introduce the main components of the system, including the mHealth application, the Open Health Connect platform, and algorithms to deliver speech enabled 3D embodied conversational agent to interact with the cancer survivors in five different languages. The system integrates cancer patients’ reported information as patient gathered health data into their digital clinical record. The value and impact of the integration will be further evaluated in the clinical study.

Use of conversational agents in rehabilitation following brain injury, disease, or stroke: a scoping review protocol

OBJECTIVE

The objective of the review is to identify peer-reviewed literature reporting the design and use of conversational agents in rehabilitation for adults with brain injury, disease, or stroke.

INTRODUCTION

Rehabilitation for adults with brain injury, disease, or stroke provides goal-directed care to overcome functional impairments and reduced independence. However, recovery can be impacted due to rehabilitation being time-limited. New therapy approaches supporting rehabilitation and self-management are warranted. Conversational agents provide personal computer-based dialogues that can be designed to meet the specific needs of clients. Interacting with a conversational agent may support rehabilitation for clients with brain injury, disease, or stroke.

INCLUSION CRITERIA

Studies that report the design or use of conversational agents in rehabilitation for adults aged over 18 years with brain injury, disease, or stroke will be considered for inclusion. Research settings may include hospitals, community settings, and homes. Eligible study types are peer-reviewed research protocols, prototype development papers, and pilot and clinical trials.

METHODS

Primary sourcing databases (MEDLINE [Ovid], Scopus, ProQuest [all databases], Web of Science) and gray literature sources will be searched with no date limitations. Only studies published in English will be considered due to feasibility limitations. Two independent reviewers will screen the retrieved papers by title and abstract, and the selected papers by full-text review. Any disagreements will be resolved by an objective arbitrator. Data to be extracted and analyzed from included papers will include details of participants, concept, context, and the study design. Results will be presented narratively and in tabular format.

Intelligent Cognitive Assistants for Attitude and Behavior Change Support in Mental Health: State-of-the-Art Technical Review

Intelligent cognitive assistant (ICA) technology is used in various domains to emulate human behavior expressed through synchronous communication, especially written conversation. Due to their ability to use individually tailored natural language, they present a powerful vessel to support attitude and behavior change. Behavior change support systems are emerging as a crucial tool in digital mental health services, and ICAs exceed in effective support, especially for stress, anxiety and depression (SAD), where ICAs guide people’s thought processes and actions by analyzing their affective and cognitive phenomena. Currently, there is no comprehensive review of such ICAs from a technical standpoint, and existing work is conducted exclusively from a psychological or medical perspective. This technical state-of-the-art review tried to discern and systematize current technological approaches and trends as well as detail the highly interdisciplinary landscape of intersections between ICAs, attitude and behavior change, and mental health, focusing on text-based ICAs for SAD. Ten papers with systems, fitting our criteria, were selected. The systems varied significantly in their approaches, with the most successful opting for comprehensive user models, classification-based assessment, personalized intervention, and dialogue tree conversational models.

A Framework for Design of Conversational Agents to Support Health Self-Care for Older Adults

OBJECTIVE

We examined the potential of conversational agents (CAs) to support older adults' self-care related to chronic illness in light of lessons learned from decades of pedagogical agent research, which investigates the impact and efficacy of CAs for a wide range of learners.

BACKGROUND

The role of CAs in education (i.e., pedagogical agents) has been long studied, but their potential for supporting self-care has received less attention, especially for older adults.

METHODS

We reviewed work on pedagogical agents and considered how it informs the design of CAs for older adults. We propose a framework for designing CAs to support older adult self-care, which organizes a review of work in this area and integration with the pedagogical agent literature.

RESULTS

Our review of the pedagogical agent literature revealed an evolution from teaching machines to interactive, social systems that influence student motivational as well as learning outcomes. To integrate this review with work on CAs and self-care, we developed a framework that specifies how self-care goals evolve with stages of an illness, communication goals that support self-care at each stage, patient needs, and requirements for CAs to support these needs. The review identified an agenda for future research on CA functions and features that help older adults accept need for self-care, establish self-care, and sustain self-care over time.

CONCLUSIONS

Integrating insights from the pedagogical agent literature with research on developing CAs for self-care defines an agenda for developing and evaluating CAs to help older adults manage illness.

Information needs and perceptions of chatbots for hypertension medication self-management: a mixed methods study

Objective

Chatbots have potential to deliver interactive self-management interventions but have rarely been studied in the context of hypertension or medication adherence. The objective of this study was to better understand patient information needs and perceptions of chatbots to support hypertension medication self-management.

Materials and Methods

Mixed methods were used to assess self-management needs and preferences for using chatbots. We purposively sampled adults with hypertension who were prescribed at least one medication. Participants completed questionnaires on sociodemographics, health literacy, self-efficacy, and technology use. Semi-structured interviews were conducted, audio-recorded, and transcribed verbatim. Quantitative data were analyzed using descriptive statistics, and qualitative data were analyzed using applied thematic analysis.

Results

Thematic saturation was met after interviewing 15 participants. Analysis revealed curiosity toward chatbots, and most perceived them as humanlike. The majority were interested in using a chatbot to help manage medications, refills, communicate with care teams, and for accountability toward self-care tasks. Despite general enthusiasm, there were concerns with chatbots providing too much information, making demands for lifestyle changes, invading privacy, and usability issues with deployment on smartphones. Those with overall positive perceptions toward chatbots were younger and taking fewer medications.

Discussion

Chatbot-related informational needs were consistent with existing self-management research, and many felt chatbots would be valuable if customizable and compatible with patient portals, pharmacies, or health apps.

Conclusion

Although most were not familiar with chatbots, patients were interested in interacting with them, but this varied. This research informs future design and functionalities of conversational interfaces to support hypertension self-management.

Voice Interface Technology Adoption by Patients With Heart Failure: Pilot Comparison Study

Background

Heart failure (HF) is associated with high mortality rates and high costs, and self-care is crucial in the management of the condition. Telehealth can promote patients’ self-care while providing frequent feedback to their health care providers about the patient’s compliance and symptoms. A number of technologies have been considered in the literature to facilitate telehealth in patients with HF. An important factor in the adoption of these technologies is their ease of use. Conversational agent technologies using a voice interface can be a good option because they use speech recognition to communicate with patients.

Objective

The aim of this paper is to study the engagement of patients with HF with voice interface technology. In particular, we investigate which patient characteristics are linked to increased technology use.

Methods

We used data from two separate HF patient groups that used different telehealth technologies over a 90-day period. Each group used a different type of voice interface; however, the scripts followed by the two technologies were identical. One technology was based on Amazon’s Alexa (Alexa+), and in the other technology, patients used a tablet to interact with a visually animated and voice-enabled avatar (Avatar). Patient engagement was measured as the number of days on which the patients used the technology during the study period. We used multiple linear regression to model engagement with the technology based on patients’ demographic and clinical characteristics and past technology use.

Results

In both populations, the patients were predominantly male and Black, had an average age of 55 years, and had HF for an average of 7 years. The only patient characteristic that was statistically different (P=.008) between the two populations was the number of medications they took to manage HF, with a mean of 8.7 (SD 4.0) for Alexa+ and 5.8 (SD 3.4) for Avatar patients. The regression model on the combined population shows that older patients used the technology more frequently (an additional 1.19 days of use for each additional year of age; P=.004). The number of medications to manage HF was negatively associated with use (−5.49; P=.005), and Black patients used the technology less frequently than other patients with similar characteristics (−15.96; P=.08).

Conclusions

Older patients’ higher engagement with telehealth is consistent with findings from previous studies, confirming the acceptability of technology in this subset of patients with HF. However, we also found that a higher number of HF medications, which may be correlated with a higher disease burden, is negatively associated with telehealth use. Finally, the lower engagement of Black patients highlights the need for further study to identify the reasons behind this lower engagement, including the possible role of social determinants of health, and potentially create technologies that are better tailored for this population.

Machine Learning for Mental Health in Social Media: Bibliometric Study

Background

Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention.

Objective

We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media.

Methods

Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described.

Results

We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated.

Conclusions

The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.

Supporting eating behaviour of community-dwelling older adults: co-design of an embodied conversational agent

In order to support community-dwelling older adults with healthy eating behaviours, Embodied Conversational Agents (ECAs) may be an effective and engaging medium. However, ECAs have not yet been found to be capable of engendering behaviour change, which is partly attributed to the absence of a match with users’ practices, needs and preferences. Hence, we describe a co-design process with older adults that informs both the content and the appearance of an ECA. Data was gathered through three consecutive iterations of co-design sessions with two groups of community-dwelling older adults in the Netherlands. Prior to the first session, participants completed a seven-day lifestyle diary. This study adds knowledge on the meaning of healthy eating, as well as on specific barriers to, and opportunities for, giving advice using an ECA in this target group. Furthermore, we translate this knowledge into general advice for designing an ECA in the context of health behaviour change, while reflecting on a co-design process with older adults.

Conversational Agents as Mediating Social Actors in Chronic Disease Management Involving Health Care Professionals, Patients, and Family Members: Multisite Single-Arm Feasibility Study

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.

Conversational Agents for Chronic Disease Self-Management: A Systematic Review

We conducted a systematic literature review to assess how conversational agents have been used to facilitate chronic disease self-management. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used. Literature was searched across five databases, and we included full-text articles that contained primary research findings for text-based conversational agents focused on self-management for chronic diseases in adults. 1,606 studies were identified, and 12 met inclusion criteria. Outcomes were largely focused on usability of conversational agents, and participants mostly reported positive attitudes with some concerns related to privacy and shallow content. In several studies, there were improvements on the Patient Health Questionnaire (p<0.05), Generalized Anxiety Disorder Scale (p=0.004), Perceived Stress Scale (p=0.048), Flourishing Scale (p=0.032), and Overall Anxiety Severity and Impairment Scale (p<0.05). There is early evidence that suggests conversational agents are acceptable, usable, and may be effective in supporting self-management, particularly for mental health.

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

BACKGROUND

Chatbots have been used in the last decade to improve access to mental health care services. Perceptions and opinions of patients influence the adoption of chatbots for health care. Many studies have been conducted to assess the perceptions and opinions of patients about mental health chatbots. To the best of our knowledge, there has been no review of the evidence surrounding perceptions and opinions of patients about mental health chatbots.

OBJECTIVE

This study aims to conduct a scoping review of the perceptions and opinions of patients about chatbots for mental health.

METHODS

The scoping review was carried out in line with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) extension for scoping reviews guidelines. Studies were identified by searching 8 electronic databases (eg, MEDLINE and Embase) in addition to conducting backward and forward reference list checking of the included studies and relevant reviews. In total, 2 reviewers independently selected studies and extracted data from the included studies. Data were synthesized using thematic analysis.

RESULTS

Of 1072 citations retrieved, 37 unique studies were included in the review. The thematic analysis generated 10 themes from the findings of the studies: usefulness, ease of use, responsiveness, understandability, acceptability, attractiveness, trustworthiness, enjoyability, content, and comparisons.

CONCLUSIONS

The results demonstrated overall positive perceptions and opinions of patients about chatbots for mental health. Important issues to be addressed in the future are the linguistic capabilities of the chatbots: they have to be able to deal adequately with unexpected user input, provide high-quality responses, and have to show high variability in responses. To be useful for clinical practice, we have to find ways to harmonize chatbot content with individual treatment recommendations, that is, a personalization of chatbot conversations is required.

Use and Effect of Web-Based Embodied Conversational Agents for Improving Eating Behavior and Decreasing Loneliness Among Community-Dwelling Older Adults: Protocol for a Randomized Controlled Trial

BACKGROUND

An unhealthy eating pattern and loneliness negatively influence quality of life in older age. Embodied conversational agents (ECAs) are a promising way to address these health behaviors in an engaging manner.

OBJECTIVE

We aim to (1) identify whether ECAs can persuade community-dwelling older adults to change their dietary behavior and whether ECA use can decrease loneliness, (2) test these pathways to effects, and (3) understand the use of an ECA.

METHODS

The web-based eHealth app PACO is a fully automated 8-week intervention in which 2 ECAs engage older adults in dialogue to motivate them to change their dietary behavior and decrease their loneliness. PACO was developed via a human-centered and stakeholder-inclusive design approach and incorporates Self-determination Theory and various behavior change techniques. For this study, an unblinded randomized controlled trial will be performed. There will be 2 cohorts, with 30 participants per cohort. Participants in the first cohort will immediately receive the PACO app for 8 weeks, while participants in the second cohort receive the PACO app after a waiting-list condition of 4 weeks. Participants will be recruited via social media, an online panel, flyers, and advertorials. To be eligible, participants must be at least 65 years of age, must not be in paid employment, and must live alone independently at home. Primary outcomes will be self-assessed via online questionnaires at intake, control, after 4 weeks, and after 8 weeks, and will include eating behavior and loneliness. In addition, the primary outcome-use-will be measured via data logs. Secondary outcomes will be measured at the same junctures, via either validated, self-assessed, online questionnaires or an optional interview.

RESULTS

As of July 2020, we have begun recruiting participants.

CONCLUSIONS

By unraveling the mechanisms behind the use of a web-based intervention with ECAs, we hope to gain a fine-grained understanding of both the effectiveness and the use of ECAs in the health context.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04510883; https://clinicaltrials.gov/ct2/show/NCT04510883.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/22186.

Computer-Assisted Avatar-Based Treatment for Dysfunctional Beliefs in Depressive Inpatients: A Pilot Study

Dysfunctional cognitions are a crucial part of depression. Cognitive therapy aims to modify dysfunctional beliefs. Typically, dysfunctional beliefs are questioned, and patients are trained to think of alternative functional beliefs. We developed a computer-assisted, avatar-based adjunct for cognitive therapy that aims to reduce dysfunctional beliefs and symptom severity. Besides, it aims to promote alternative functional beliefs. In a randomized controlled trial with 34 patients diagnosed with major depression currently undergoing inpatient treatment at the university psychiatric hospital in Regensburg, Germany, participants were randomly assigned to receive either treatment as usual (TAU) or computer-assisted avatar-based treatment for dysfunctional beliefs (CAT-DB) in addition to TAU. In CAT-DB participants are faced with a virtual avatar expressing their personal dysfunctional beliefs. Participants are asked to contradict these and express alternative functional beliefs. Assessments of conviction of dysfunctional beliefs, functional beliefs and symptom severity were done shortly before the intervention (pre-treatment), right after the intervention (post-treatment) and 14 days later (follow-up). The reduction in conviction of dysfunctional beliefs and symptom severity, and the increase in conviction of alternative functional beliefs at post-treatment and follow-up were significantly greater for the group receiving CAT-DB. Our study provides an indication in favor of the effectiveness of CAT-DB for depressive patients. It is a simple tool that could support classical cognitive therapy. Further studies at different centres, with larger sample sizes and varying therapeutic contexts are required to prove the effectiveness of our intervention.

Asynchronously Embedding Psychological Test Questions into Human–Robot Conversations for User Profiling

Psychological variables of a person (e.g., cognitive abilities, personality traits, emotional states, and preferences) are valuable information that can be utilized by social robots to offer personalized human–robot interaction. These variables are often latent and inferred indirectly from a third-person perspective based on an individual’s behavioral manifestations (e.g., facial emotion expressions), and hence the true values of inferred psychological variables remain unknown to a robot observer. Although earlier studies have employed robot-administered psychological tests to infer psychological variables based on an individual’s first-person responses, these tests were formally presented and could be tedious to some users. To leverage the validity and reliability of well-established psychological tests for user profiling with ease, the present study examined the possibility of asynchronously embedding psychological test questions into casual human–robot conversations. In our experiment using a big-five personality inventory, the verbal responses from users to these asynchronous test questions were then compared with the written responses to the same personality test. The personality measures estimated from the two approaches correlated strongly in a young adult population but only moderately in an older population. These findings demonstrate the validity of the proposed asynchronous method for psychological testing in human–agent interactions and suggest some caveats when this testing method is applied to older adults or other special populations.

The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review

BACKGROUND

The high demand for health care services and the growing capability of artificial intelligence have led to the development of conversational agents designed to support a variety of health-related activities, including behavior change, treatment support, health monitoring, training, triage, and screening support. Automation of these tasks could free clinicians to focus on more complex work and increase the accessibility to health care services for the public. An overarching assessment of the acceptability, usability, and effectiveness of these agents in health care is needed to collate the evidence so that future development can target areas for improvement and potential for sustainable adoption.

OBJECTIVE

This systematic review aims to assess the effectiveness and usability of conversational agents in health care and identify the elements that users like and dislike to inform future research and development of these agents.

METHODS

PubMed, Medline (Ovid), EMBASE (Excerpta Medica dataBASE), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and the Association for Computing Machinery Digital Library were systematically searched for articles published since 2008 that evaluated unconstrained natural language processing conversational agents used in health care. EndNote (version X9, Clarivate Analytics) reference management software was used for initial screening, and full-text screening was conducted by 1 reviewer. Data were extracted, and the risk of bias was assessed by one reviewer and validated by another.

RESULTS

A total of 31 studies were selected and included a variety of conversational agents, including 14 chatbots (2 of which were voice chatbots), 6 embodied conversational agents (3 of which were interactive voice response calls, virtual patients, and speech recognition screening systems), 1 contextual question-answering agent, and 1 voice recognition triage system. Overall, the evidence reported was mostly positive or mixed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations of the agents highlighted in specific qualitative feedback.

CONCLUSIONS

The studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting are necessary to more accurately evaluate the usefulness of the agents in health care and identify key areas for improvement. Further research should also analyze the cost-effectiveness, privacy, and security of the agents.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/16934.

Better, Virtually: the Past, Present, and Future of Virtual Reality Cognitive Behavior Therapy

Virtual reality (VR) is an immersive technology capable of creating a powerful, perceptual illusion of being present in a virtual environment. VR technology has been used in cognitive behavior therapy since the 1990s and accumulated an impressive evidence base, yet with the recent release of consumer VR platforms came a true paradigm shift in the capabilities and scalability of VR for mental health. This narrative review summarizes the past, present, and future of the field, including milestone studies and discussions on the clinical potential of alternative embodiment, gamification, avatar therapists, virtual gatherings, immersive storytelling, and more. Although the future is hard to predict, clinical VR has and will continue to be inherently intertwined with what are now rapid developments in technology, presenting both challenges and exciting opportunities to do what is not possible in the real world.

Improving adherence to an online intervention for low mood with a virtual coach: study protocol of a pilot randomized controlled trial

Background

Internet-based cognitive-behavioral therapy (iCBT) is more effective when it is guided by human support than when it is unguided. This may be attributable to higher adherence rates that result from a positive effect of the accompanying support on motivation and on engagement with the intervention. This protocol presents the design of a pilot randomized controlled trial that aims to start bridging the gap between guided and unguided interventions. It will test an intervention that includes automated support delivered by an embodied conversational agent (ECA) in the form of a virtual coach.

Methods/design

The study will employ a pilot two-armed randomized controlled trial design. The primary outcomes of the trial will be (1) the effectiveness of iCBT, as supported by a virtual coach, in terms of improved intervention adherence in comparison with unguided iCBT, and (2) the feasibility of a future, larger-scale trial in terms of recruitment, acceptability, and sample size calculation. Secondary aims will be to assess the virtual coach’s effect on motivation, users’ perceptions of the virtual coach, and general feasibility of the intervention as supported by a virtual coach. We will recruit N = 70 participants from the general population who wish to learn how they can improve their mood by using Moodbuster Lite, a 4-week cognitive-behavioral therapy course. Candidates with symptoms of moderate to severe depression will be excluded from study participation. Included participants will be randomized in a 1:1 ratio to either (1) Moodbuster Lite with automated support delivered by a virtual coach or (2) Moodbuster Lite without automated support. Assessments will be taken at baseline and post-study 4 weeks later.

Discussion

The study will assess the preliminary effectiveness of a virtual coach in improving adherence and will determine the feasibility of a larger-scale RCT. It could represent a significant step in bridging the gap between guided and unguided iCBT interventions.

Trial registration

Netherlands Trial Register (NTR) NL8110. Registered on 23 October 2019.

Conversational Agents in Health Care: Scoping Review and Conceptual Analysis

BACKGROUND

Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care.

OBJECTIVE

This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application.

METHODS

We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms "conversational agents," "conversational AI," "chatbots," and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis.

RESULTS

The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education.

CONCLUSIONS

The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence-driven, and smartphone app-delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents' formats, focusing on their acceptability, safety, and effectiveness.

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis

BACKGROUND

Virtual humans (VH) are computer-generated characters that appear humanlike and simulate face-to-face conversations using verbal and nonverbal cues. Unlike formless conversational agents, like smart speakers or chatbots, VH bring together the capabilities of both a conversational agent and an interactive avatar (computer-represented digital characters). Although their use in patient-facing systems has garnered substantial interest, it is unknown to what extent VH are effective in health applications.

OBJECTIVE

The purpose of this review was to examine the effectiveness of VH in patient-facing systems. The design and implementation characteristics of these systems were also examined.

METHODS

Electronic bibliographic databases were searched for peer-reviewed articles with relevant key terms. Studies were included in the systematic review if they designed or evaluated VH in patient-facing systems. Of the included studies, studies that used a randomized controlled trial to evaluate VH were included in the meta-analysis; they were then summarized using the PICOTS framework (population, intervention, comparison group, outcomes, time frame, setting). Summary effect sizes, using random-effects models, were calculated, and the risk of bias was assessed.

RESULTS

Among the 8,125 unique records identified, 53 articles describing 33 unique systems, were qualitatively, systematically reviewed. Two distinct design categories emerged - simple VH and VH augmented with health sensors and trackers. Of the 53 articles, 16 (26 studies) with 44 primary and 22 secondary outcomes were included in the meta-analysis. Meta-analysis of the 44 primary outcome measures revealed a significant difference between intervention and control conditions, favoring the VH intervention (SMD = .166, 95% CI .039-.292, P=.012), but with evidence of some heterogeneity, I2=49.3%. There were more cross-sectional (k=15) than longitudinal studies (k=11). The intervention was delivered using a personal computer in most studies (k=18), followed by a tablet (k=4), mobile kiosk (k=2), head-mounted display (k=1), and a desktop computer in a community center (k=1).

CONCLUSIONS

We offer evidence for the efficacy of VH in patient-facing systems. Considering that studies included different population and outcome types, more focused analysis is needed in the future. Future studies also need to identify what features of virtual human interventions contribute toward their effectiveness.

Robot applications for autism: a comprehensive review

PURPOSE

Technological advances in robotics have brought about exciting developments in different areas such as education, training, and therapy. Recent research has suggested that the robot can be even more effective in rehabilitation, therapy, and education for individuals with Autism Spectrum Disorder (ASD). In this paper, a comprehensive review of robotic technology for children with ASD is presented wherein a large number of journals and conference proceedings in science and engineering search engines' databases were implicated.

MATERIALS AND METHODS

A search for related literature was conducted in three search engines' databases, Web of Science, Scopus, and IEEE Xplore. Thematic keywords were used to identify articles in the recent ten years in titles, keywords, and abstracts. The retrieved articles were filtered, analysed, and evaluated based on specific inclusion and exclusion criteria.

RESULTS

A total of 208 studies were retrieved, while 166 met the inclusion criteria. The selected studies were reviewed according to the type of robot, the participants, objectives, and methods. 68 robots were used in all studies, NAO robot was used in 30.5% of those studies. The total number of participants in all studies was 1671. The highest percentage of the studies reviewed were dedicated to augmenting the learning skills.

CONCLUSIONS

Robots and the associated schemes were used to determine their feasibility and validity for augmenting the learning skills of autistic children. Most of the studies reviewed were focused on improving the social communication skills of autistic children and measuring the extent of robot mitigation of stereotyped autistic behaviours.Implications for rehabilitationSocial robots are not considered as promising tools to be utilized for rehabilitation of autistic children only, but also has been used for children and young people with severe intellectual disability.Rehabilitation for individuals with ASD using robots can augment their cognitive and social skills, but further studies should be conducted to clarify its effectiveness based on other factors such as sex, age and IQ of the participates.Robotic-based rehabilitation is not limited to the physical robots only, but virtual robots have been used also, whereas each of which can be used individually or simultaneously. However, further study is required to assess the extent of its efficiency and effectiveness for both cases.

Acceptability of an Embodied Conversational Agent for Type 2 Diabetes Self-Management Education and Support via a Smartphone App: Mixed Methods Study

BACKGROUND

Embodied conversational agents (ECAs) are increasingly used in health care apps; however, their acceptability in type 2 diabetes (T2D) self-management apps has not yet been investigated.

OBJECTIVE

This study aimed to evaluate the acceptability of the ECA (Laura) used to deliver diabetes self-management education and support in the My Diabetes Coach (MDC) app.

METHODS

A sequential mixed methods design was applied. Adults with T2D allocated to the intervention arm of the MDC trial used the MDC app over a period of 12 months. At 6 months, they completed questions assessing their interaction with, and attitudes toward, the ECA. In-depth qualitative interviews were conducted with a subsample of the participants from the intervention arm to explore their experiences of using the ECA. The interview questions included the participants' perceptions of Laura, including their initial impression of her (and how this changed over time), her personality, and human character. The quantitative and qualitative data were interpreted using integrated synthesis.

RESULTS

Of the 93 intervention participants, 44 (47%) were women; the mean (SD) age of the participants was 55 (SD 10) years and the baseline glycated hemoglobin A1c level was 7.3% (SD 1.5%). Overall, 66 of the 93 participants (71%) provided survey responses. Of these, most described Laura as being helpful (57/66, 86%), friendly (57/66, 86%), competent (56/66, 85%), trustworthy (48/66, 73%), and likable (40/66, 61%). Some described Laura as not real (18/66, 27%), boring (26/66, 39%), and annoying (20/66, 30%). Participants reported that interacting with Laura made them feel more motivated (29/66, 44%), comfortable (24/66, 36%), confident (14/66, 21%), happy (11/66, 17%), and hopeful (8/66, 12%). Furthermore, 20% (13/66) of the participants were frustrated by their interaction with Laura, and 17% (11/66) of the participants reported that interacting with Laura made them feel guilty. A total of 4 themes emerged from the qualitative data (N=19): (1) perceived role: a friendly coach rather than a health professional; (2) perceived support: emotional and motivational support; (3) embodiment preference acceptability of a human-like character; and (4) room for improvement: need for greater congruence between Laura's words and actions.

CONCLUSIONS

These findings suggest that an ECA is an acceptable means to deliver T2D self-management education and support. A human-like character providing ongoing, friendly, nonjudgmental, emotional, and motivational support is well received. Nevertheless, the ECA can be improved by increasing congruence between its verbal and nonverbal communication and accommodating user preferences.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry CTRN12614001229662; https://tinyurl.com/yxshn6pd.

Effectiveness and Safety of Using Chatbots to Improve Mental Health: Systematic Review and Meta-Analysis

Background

The global shortage of mental health workers has prompted the utilization of technological advancements, such as chatbots, to meet the needs of people with mental health conditions. Chatbots are systems that are able to converse and interact with human users using spoken, written, and visual language. While numerous studies have assessed the effectiveness and safety of using chatbots in mental health, no reviews have pooled the results of those studies.

Objective

This study aimed to assess the effectiveness and safety of using chatbots to improve mental health through summarizing and pooling the results of previous studies.

Methods

A systematic review was carried out to achieve this objective. The search sources were 7 bibliographic databases (eg, MEDLINE, EMBASE, PsycINFO), the search engine “Google Scholar,” and backward and forward reference list checking of the included studies and relevant reviews. Two reviewers independently selected the studies, extracted data from the included studies, and assessed the risk of bias. Data extracted from studies were synthesized using narrative and statistical methods, as appropriate.

Results

Of 1048 citations retrieved, we identified 12 studies examining the effect of using chatbots on 8 outcomes. Weak evidence demonstrated that chatbots were effective in improving depression, distress, stress, and acrophobia. In contrast, according to similar evidence, there was no statistically significant effect of using chatbots on subjective psychological wellbeing. Results were conflicting regarding the effect of chatbots on the severity of anxiety and positive and negative affect. Only two studies assessed the safety of chatbots and concluded that they are safe in mental health, as no adverse events or harms were reported.

Conclusions

Chatbots have the potential to improve mental health. However, the evidence in this review was not sufficient to definitely conclude this due to lack of evidence that their effect is clinically important, a lack of studies assessing each outcome, high risk of bias in those studies, and conflicting results for some outcomes. Further studies are required to draw solid conclusions about the effectiveness and safety of chatbots.

Trial Registration

PROSPERO International Prospective Register of Systematic Reviews CRD42019141219; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019141219

Acceptance of virtual agents in a homecare context: Evaluation of excessive daytime sleepiness in apneic patients during interventions by continuous positive airway pressure (CPAP) providers

Sleep apnea syndrome treatment relies primarily on continuous positive airway pressure, which requires a homecare follow-up. Excessive daytime sleepiness is a key symptom to be measured during follow-up, and there is a need for more easy-to-administer, ecological and engaging tools to assess it. Virtual agents have shown their effectiveness in performing clinical evaluation in a medical environment. A virtual agent assessing excessive daytime sleepiness via the Epworth Sleepiness Scale has been tested at home by 102 patients treated by continuous positive airway pressure (CPAP) and their homecare providers. Acceptance and trustworthiness of the agent, as well as the influence of demographics and clinical characteristics of patients, were investigated. Results showed that both patients and homecare providers reported a positive attitude towards the virtual agent, which was found to be usable, satisfactory, credible and benevolent. Homecare providers reported that it was not a burden to their working routine. Positive regression coefficients were found between patients' and homecare providers' attitudes towards the virtual agent. We found no influence of patients' demographics and clinical characteristics on their acceptance of the virtual agent. This shows that virtual agents are well accepted by patients and homecare providers, independently of their demographics and clinical characteristics. Positive regression coefficients observed between patients' and homecare providers' perceptions of the agent indicate that the opinions of both influence acceptance of the virtual agent in a homecare context. Virtual agents are new innovative digital solutions to be considered in homecare follow-up of apneic patients treated by CPAP.

Exploring Smart Agents for the Interaction with Multimodal Mediated Environments

After conversational agents have been made available to the broader public, we speculate that applying them as a mediator for adaptive environments reduces control complexity and increases user experience by providing a more natural interaction. We implemented and tested four agents, each of them differing in their system intelligence and input modality, as personal assistants for Mediated Atmospheres, an adaptive smart office prototype. They were evaluated in a user study ( N = 33 ) to collect subjective and objective measures. Results showed that a smartphone application was the most favorable system, followed by conversational text and voice agents that were perceived as being more engaging and intelligent than a non-conversational voice agent. Significant differences were observed between native and non-native speakers in both subjective and objective measures. Our findings reveal the potential of conversational agents for the interaction with adaptive environments to reduce work and information overload.

Technical Metrics Used to Evaluate Health Care Chatbots: Scoping Review

BACKGROUND

Dialog agents (chatbots) have a long history of application in health care, where they have been used for tasks such as supporting patient self-management and providing counseling. Their use is expected to grow with increasing demands on health systems and improving artificial intelligence (AI) capability. Approaches to the evaluation of health care chatbots, however, appear to be diverse and haphazard, resulting in a potential barrier to the advancement of the field.

OBJECTIVE

This study aims to identify the technical (nonclinical) metrics used by previous studies to evaluate health care chatbots.

METHODS

Studies were identified by searching 7 bibliographic databases (eg, MEDLINE and PsycINFO) in addition to conducting backward and forward reference list checking of the included studies and relevant reviews. The studies were independently selected by two reviewers who then extracted data from the included studies. Extracted data were synthesized narratively by grouping the identified metrics into categories based on the aspect of chatbots that the metrics evaluated.

RESULTS

Of the 1498 citations retrieved, 65 studies were included in this review. Chatbots were evaluated using 27 technical metrics, which were related to chatbots as a whole (eg, usability, classifier performance, speed), response generation (eg, comprehensibility, realism, repetitiveness), response understanding (eg, chatbot understanding as assessed by users, word error rate, concept error rate), and esthetics (eg, appearance of the virtual agent, background color, and content).

CONCLUSIONS

The technical metrics of health chatbot studies were diverse, with survey designs and global usability metrics dominating. The lack of standardization and paucity of objective measures make it difficult to compare the performance of health chatbots and could inhibit advancement of the field. We suggest that researchers more frequently include metrics computed from conversation logs. In addition, we recommend the development of a framework of technical metrics with recommendations for specific circumstances for their inclusion in chatbot studies.

Optimizing Students' Mental Health and Academic Performance: AI-Enhanced Life Crafting

One in three university students experiences mental health problems during their study. A similar percentage leaves higher education without obtaining the degree for which they enrolled. Research suggests that both mental health problems and academic underperformance could be caused by students lacking control and purpose while they are adjusting to tertiary education. Currently, universities are not designed to cater to all the personal needs and mental health problems of large numbers of students at the start of their studies. Within the literature aimed at preventing mental health problems among students (e.g., anxiety or depression), digital forms of therapy recently have been suggested as potentially scalable solutions to address these problems. Integrative psychological artificial intelligence (AI) in the form of a chatbot, for example, shows great potential as an evidence-based solution. At the same time, within the literature aimed at improving academic performance, the online life-crafting intervention in which students write about values and passions, goals, and goal-attainment plans has shown to improve the academic performance and retention rates of students. Because the life-crafting intervention is delivered through the curriculum and doesn't bear the stigma that is associated with therapy, it can reach larger populations of students. But life-crafting lacks the means for follow-up or the interactiveness that online AI-guided therapy can offer. In this narrative review, we propose to integrate the current literature on chatbot interventions aimed at the mental health of students with research about a life-crafting intervention that uses an inclusive curriculum-wide approach. When a chatbot asks students to prioritize both academic as well as social and health-related goals and provides personalized follow-up coaching, this can prevent -often interrelated- academic and mental health problems. Right on-time delivery, and personalized follow-up questions enhance the effects of both -originally separated- intervention types. Research on this new combination of interventions should use design principles that increase user-friendliness and monitor the technology acceptance of its participants.

Cognitive plausibility in voice-based AI health counselors

Voice-based personal assistants using artificial intelligence (AI) have been widely adopted and used in home-based settings. Their success has created considerable interest for its use in healthcare applications; one area of prolific growth in AI is that of voice-based virtual counselors for mental health and well-being. However, in spite of its promise, building realistic virtual counselors to achieve higher-order maturity levels beyond task-based interactions presents considerable conceptual and pragmatic challenges. We describe one such conceptual challenge-cognitive plausibility, defined as the ability of virtual counselors to emulate the human cognitive system by simulating how a skill or function is accomplished. An important cognitive plausibility consideration for voice-based agents is its ability to engage in meaningful and seamless interactive communication. Drawing on a broad interdisciplinary research literature and based on our experiences with developing two voice-based (voice-only) prototypes that are in the early phases of testing, we articulate two conceptual considerations for their design and use-conceptualizing voice-based virtual counselors as communicative agents and establishing virtual co-presence. We discuss why these conceptual considerations are important and how it can lead to the development of voice-based counselors for real-world use.

Social robots as treatment agents: Pilot randomized controlled trial to deliver a behavior change intervention

Social robots are increasingly demonstrating effectiveness as low-intensity behavior change agents. Key targets for these behavioral interventions include daily lifestyle behaviors with significant health consequences, such as the consumption of high-calorie foods and drinks ('snacks'). A pilot randomized controlled trial using a stepped-wedge design was conducted to determine the efficacy of a motivational intervention by an autonomous robot, to help reduce high-calorie snacks. Twenty-six adults were randomized to receive Immediate or 4-week Delayed treatment, with assessments at Baseline and Weeks 4 and 8. The treatment comprised motivation enhancement and self-management training using mental imagery (Functional Imagery Training). A significant condition by time effect for snack episode reduction was obtained, F(2, 32.06) = 4.30, p = .022. The Immediate condition significantly reduced snacking between Baseline and Week 4 (d = -1.06), while the Delayed condition did not (d = -0.08). Immediate participants maintained their improvement between Weeks 4 and 8 (d = -0.18), and Delayed participants then showed a significant fall (d = -1.42). Overall, 'Immediate' participants decreased their snack episodes by 54% and 'Delayed' decreased by 62% from Baseline to Week 8, and an average weight reduction of 4.4 kg was seen across over the first 2 weeks of treatment. Four weeks after starting the intervention, both conditions had significant increases in perceived confidence to control snack intake for time duration, specific scenarios and emotional states (d = 0.61 to 1.42). Working alliance was significantly correlated with reduced snack episodes. The pilot's results appear to suggest that the robot-delivered intervention may be as effective as a human clinician delivering a similar intervention. The robot-delivered pilot achieved similar snack episode reduction in the first four weeks (FIT-R, 55%) when compared with the human-delivered version by a trained clinician (FIT-H, 49%). Overall, the results provide preliminary evidence for an autonomous social robot to deliver a low-intensity treatment on dietary intake without the need for human intervention. Future trials should extend the deployment of the robot-delivered intervention protocol to other low-intensity behavioral outcomes.

A Smartphone-Based Health Care Chatbot to Promote Self-Management of Chronic Pain (SELMA): Pilot Randomized Controlled Trial

BACKGROUND

Ongoing pain is one of the most common diseases and has major physical, psychological, social, and economic impacts. A mobile health intervention utilizing a fully automated text-based health care chatbot (TBHC) may offer an innovative way not only to deliver coping strategies and psychoeducation for pain management but also to build a working alliance between a participant and the TBHC.

OBJECTIVE

The objectives of this study are twofold: (1) to describe the design and implementation to promote the chatbot painSELfMAnagement (SELMA), a 2-month smartphone-based cognitive behavior therapy (CBT) TBHC intervention for pain self-management in patients with ongoing or cyclic pain, and (2) to present findings from a pilot randomized controlled trial, in which effectiveness, influence of intention to change behavior, pain duration, working alliance, acceptance, and adherence were evaluated.

METHODS

Participants were recruited online and in collaboration with pain experts, and were randomized to interact with SELMA for 8 weeks either every day or every other day concerning CBT-based pain management (n=59), or weekly concerning content not related to pain management (n=43). Pain-related impairment (primary outcome), general well-being, pain intensity, and the bond scale of working alliance were measured at baseline and postintervention. Intention to change behavior and pain duration were measured at baseline only, and acceptance postintervention was assessed via self-reporting instruments. Adherence was assessed via usage data.

RESULTS

From May 2018 to August 2018, 311 adults downloaded the SELMA app, 102 of whom consented to participate and met the inclusion criteria. The average age of the women (88/102, 86.4%) and men (14/102, 13.6%) participating was 43.7 (SD 12.7) years. Baseline group comparison did not differ with respect to any demographic or clinical variable. The intervention group reported no significant change in pain-related impairment (P=.68) compared to the control group postintervention. The intention to change behavior was positively related to pain-related impairment (P=.01) and pain intensity (P=.01). Working alliance with the TBHC SELMA was comparable to that obtained in guided internet therapies with human coaches. Participants enjoyed using the app, perceiving it as useful and easy to use. Participants of the intervention group replied with an average answer ratio of 0.71 (SD 0.20) to 200 (SD 58.45) conversations initiated by SELMA. Participants' comments revealed an appreciation of the empathic and responsible interaction with the TBHC SELMA. A main criticism was that there was no option to enter free text for the patients' own comments.

CONCLUSIONS

SELMA is feasible, as revealed mainly by positive feedback and valuable suggestions for future revisions. For example, the participants' intention to change behavior or a more homogenous sample (eg, with a specific type of chronic pain) should be considered in further tailoring of SELMA.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00017147; https://tinyurl.com/vx6n6sx, Swiss National Clinical Trial Portal: SNCTP000002712; https://www.kofam.ch/de/studienportal/suche/70582/studie/46326.

Measuring Alliance Toward Embodied Virtual Therapists in the Era of Automated Treatments With the Virtual Therapist Alliance Scale (VTAS): Development and Psychometric Evaluation

BACKGROUND

Automated virtual reality exposure therapies (VRETs) are self-help treatments conducted by oneself and supported by a virtual therapist embodied visually and/or with audio feedback. This simulates many of the nonspecific relational elements and common factors present in face-to-face therapy and may be a means of improving adherence to and efficacy of self-guided treatments. However, little is known about alliance toward the virtual therapist, despite alliance being an important predictor of treatment outcome.

OBJECTIVE

In this study, we aimed to evaluate the first alliance instrument developed for use with embodied virtual therapists in an automated treatment format-the Virtual Therapist Alliance Scale (VTAS)-by (1) assessing its psychometric properties, (2) verifying the dimensionality of the scale, and (3) determining the predictive ability of the scale with treatment outcome.

METHODS

A psychometric evaluation and exploratory factor analysis of the VTAS was conducted using data from two samples of spider-fearful patients treated with VRET and the help of an embodied, voice-based virtual therapist (n=70). Multiple regression models and bivariate correlations were used to assess the VTAS relationship with treatment outcome, according to self-reported fear and convergence with presence and user-friendliness process measures.

RESULTS

The VTAS showed a sound two-factor solution composed of a primary factor covering task, goal, and copresence; adequate internal consistency; and good convergent validity, including moderate correlation (r=.310, P=.01) with outcomes over follow-up.

CONCLUSIONS

These preliminary results suggest that alliance toward a virtual therapist is a significant predictor of treatment outcome, favors the importance of a task-goal over bond-factor, and should be explored in studies with larger sample sizes and in additional forms of embodiment.