Design Features of Embodied Conversational Agents in eHealth
A Literature Review
Bibliographic Data
| ID | 21642332 |
|---|---|
| Authors | Silke ter Stal (0000-0001-9458-717X, Roessingh Research and Development, corresponding author), Lean L Kramer (0000-0002-1409-2853, Wageningen University & Research), Monique Tabak (0000-0001-5082-1112, Roessingh Research and Development), Harm op den Akker (0000-0001-6312-6063, Roessingh Research and Development), Hermie Hermens (0000-0002-3065-3876, Roessingh Research and Development) |
| Year | 2020 |
| Volume | 138 |
| Pages | 102409 |
| Publication date | 2020-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Human-Computer Studies (JOURNAL) |
| Journal identifiers | ISSN: 1071-5819 • E-ISSN: 1095-9300 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijhcs.2020.102409 |
| OpenAlex | W3005092074 |
| Language | EN |
| Citations received | 19 |
| References cited | 32 |
Embodied conversational agents (ECAs) are gaining interest to elicit user engagement and stimulate actual use of eHealth applications. In this literature review, we identify the researched design features for ECAs in eHealth, the outcome variables that were used to measure the effect of these design features and what the found effects for each variable were. Searches were performed in Scopus, ACM Digital Library, PsychINFO, Pubmed and IEEE Xplore Digital Library, resulting in 1284 identified articles of which 33 articles were included. The agents speech and/or textual output and its facial and gaze expressions were the most common design features. Little research was performed on the agent's looks. The measured effect of these design features was often on the perception of the agent's and user’s characteristics, relation with the agent, system usage, intention to use, usability and behaviour change. Results show that emotion and relational behaviour seem to positively affect the perception of the agents characteristics and that relational behaviour also seems to positively affect the relation with the agent, usability and intention to use. However, these design features do not necessarily lead to behaviour change. This review showed that consensus on design features of ECAs in eHealth is far from established. Follow-up research should include more research on the effects of all design features, especially research on the effects in a long-term, daily life setting, and replication of studies on the effects of design features performed in other contexts than eHealth
Dialog box · Dialog system · Digital library · eHealth · Embodied cognition · Health care · Human–computer interaction · MEDLINE · Perception · Scopus · Usability · World Wide Web · AI in Service Interactions · Computer Science · Digital Mental Health Interventions · Psychology · Social Robot Interaction and HRI · Artificial Intelligence
Why Would I Befriend a Bot? Assessing Factors Influencing the Usage of Social Chatbots for Digital Natives
Development of a digital biomarker and intervention for subclinical depression
User Requirements Analysis of an Embodied Conversational Agent for Coaching Older Adults to Choose Active and Healthy Ageing Behaviors during the Transition to Retirement
Toward the Integration of Technology-Based Interventions in the Care Pathway for People with Dementia
Short and Long-Term Innovations on Dietary Behavior Assessment and Coaching
U.S. and Japanese Consumer Attitudes Toward Tailored and Targeted Communication with Human and Chatbot Agents
Investigating the use of speech-based conversational agents for life coaching
The human side of human-chatbot interaction
Imagining future digital assistants at work
Information quality of conversational agents in healthcare
A staged approach to the development of an embodied conversational agent to support wellbeing after injury
Psychological insights into the research and practice of embodied conversational agents, chatbots and social assistive robots
Commercial video games as a resource for mental health
Embodied conversational agents for collecting patient-reported data
What influences algorithmic decision-making? A systematic literature review on algorithm aversion
Effectiveness of embodied conversational agents for managing academic stress at an Indian University (ARU) during Covid‐19
Generic Vs. Personalised Robot Feedback in Health-Related Recommendation
The Impact of Virtual Humans on Psychosomatic Medicine
Adoption Factors and Moderating Effects of Age and Gender That Influence the Intention to Use a Non-Directive Reflective Coaching Chatbot
| Unique citing works | 19 |
|---|---|
| Citations per year | 3,8 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 19 |