Assistive Conversational Agent for Health Coaching: A Validation Study
Abstract
Objective Poor lifestyle represents a health risk factor and is the leading cause of morbidity and chronic conditions. The impact of poor lifestyle can be significantly altered by individual's behavioral modification. Although there are abundant lifestyle promotion applications and tools, they are still limited in providing tailored social support that goes beyond their predefined functionalities. In addition, virtual coaching approaches are still unable to handle user emotional needs. Our approach presents a human–virtual agent mediated system that leverages the conversational agent to handle menial caregiver's works by engaging users (e.g., patients) in a conversation with the conversational agent. The dialog used a natural conversation to interact with users, delivered by the conversational agent and handled with a finite state machine automaton. Our research differs from existing approaches that replace a human coach with a fully automated assistant on user support. The methodology allows users to interact with the technology and access health-related interventions. To assist physicians, the conversational agent gives weighting to user's adherence, based on prior defined conditions. Materials and Methods This article describes the design and validation of CoachAI, a conversational agent-assisted health coaching system to support health intervention delivery to individuals or groups. CoachAI instantiates a text-based health care conversational agent system that bridges the remote human coach and the users. Results We will discuss our approach and highlight the outcome of a 1-month validation study on physical activity, healthy diet, and stress coping. The study validates technology aspects of our human–virtual agent mediated health coaching system. We present the intervention settings and findings from the study. In addition, we present some user-experience validation results gathered during or after the experimentation. Conclusions The study provided a set of dimensions when building a human–conversational agent powered health intervention tool. The results provided interesting insights when using human–conversational agent mediated approach in health coaching systems. The findings revealed that users who were highly engaged were also more adherent to conversational-agent activities. This research made key contributions to the literature on techniques in providing social, yet tailored health coaching support: (1) identifying habitual patterns to understand user preferences; (2) the role of a conversational agent in delivering health promoting microactivities; (3) building the technology while adhering to individuals' daily messaging routine; and (4) a socio-technical system that fits with the role of conversational agent as an assistive component. Future Work Future improvements will consider building the activity recommender based on users' interaction data and integrating users' dietary pattern and emotional wellbeing into the initial user clustering by leveraging information and communication technology approaches (e.g., machine learning). We will integrate a sentiment analysis capability to gather further data about individuals and report these data to the caregiver.
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A validation study found the CoachAI chatbot to be effective, especially among users with high engagement levels.
“Another validation study [46] reported on the CoachAI chatbot, which provided social and tailored health coaching support, and found this chatbot to be effective, especially among users with high engagement levels.”
One chatbot communicated daily, weekly, on weekends or weekdays, or at a scheduled date and time.
“One chatbot communicated only on a weekly basis [20], and 1 communicated daily, weekly, on weekends or weekdays or at a scheduled date and time [35].”
From A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss (2021) · cites this paper as [35]
Investigated uses of conversational agents include triage, diagnostics, counseling, health promotion, and training of health care professionals.
“The investigated uses for these systems include triage, diagnostics, counseling, health promotion, and training of health care professionals [1,4,11-16].”
From Effectiveness of Conversational Agents (Virtual Assistants) in Health Care: Protocol for a Systematic Review (2020) · cites this paper as [16]
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- Authors
- Fadhil, A., Wang, Y., & Reiterer, H.
- Affiliations
- University of Trento (IT)
- University of Konstanz (DE)
- Corresponding author
- Fadhil, Ahmed
- Venue
- Methods of Information in Medicine
- Publisher
- Georg Thieme Verlag KG
- Published
- 2019-05-22
- Volume
- 58
- Issue
- 01
- Pages
- 009-023
- ISSN
- 0026-1270 (ISSN-L)
- 2511-705X
- OpenAlex
- W2946084149
- Cited by
- 77
- References
- 42
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 13 · 2023: 10 · 2022: 10 · 2021: 13 · 2020: 10 · 2019: 2
- FWCI
- 11.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Methods of Information in Medicine (publishedVersion)
- PubMed (publishedVersion)
- KOPS (University of Konstanz) (submittedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- chatbots, health coaching, physical activity, healthy eating, stress coping, user adherence, user engagement, social support, assistive technology, user experience, lifestyle modification, user clustering, sentiment analysis, user preferences
- MeSH terms
- Mentoring, Communication, Diet, Health, Humans, Health Knowledge, Attitudes, Practice, Mental Health, Motivation, Patient Compliance, Surveys and Questionnaires, Exercise
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