Automated interventions for multiple health behaviors using conversational agents
Open accessAbstract
Objective An automated health counselor agent was designed to promote both physical activity and fruit and vegetable consumption through a series of simulated conversations with users on their home computers.Methods The agent was evaluated in a 4-arm randomized trial of a two-month daily contact intervention comparing: (a) physical activity; (b) fruit and vegetable consumption; (c) both interventions; and (d) a non-intervention control. Physical activity was assessed using daily pedometer steps. Daily servings of fruit and vegetables were assessed using the NIH/NCI self-report Fruit and Vegetable Scan.Results Participants in the physical activity intervention increased their walking on average compared to the control group, while those in the fruit and vegetable intervention and combined intervention decreased walking. Participants in the fruit and vegetable intervention group consumed significantly more servings per day compared to those in the control group, and those in the combined intervention reported consuming more compared to those in the control group.Conclusion Automated health intervention software designed for efficient re-use is effective at changing health behavior.Practice implications Automated health behavior change interventions can be designed to facilitate translation and adaptation across multiple behaviors.
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Several randomized controlled trials of interventions involving embodied conversational agents have shown significant improvements in physical activity, fruit and vegetable consumption, and accessibility to online health information, among other outcomes.
“Several randomized controlled trials of interventions involving embodied conversational agents have shown significant improvements in physical activity, fruit and vegetable consumption, and accessibility to online health information, among other outcomes.”
From Conversational agents in healthcare: a systematic review (2018) · cites this paper as [4]
The Abstract (Methods) describes one randomized trial, not several. The Results (§3.1–3.3) report a significant improvement in fruit and vegetable consumption and faster growth in walking steps for the physical-activity group, but no significant difference in self-reported physical activity. Our copy does not report an outcome concerning accessibility to online health information. (LLM, conf=0.90)
Experimental — accuracy not yet measuredOver the last two decades, a solid body of evidence has shown the potential benefits of using embodied conversational agents for health-related purposes.
“Over the last two decades, a solid body of evidence has shown the potential benefits of using embodied conversational agents for health-related purposes.”
From Conversational agents in healthcare: a systematic review (2018) · cites this paper as [4]
The Results and Discussion report benefits from this conversational-agent intervention, particularly increased fruit and vegetable consumption compared with the control group. The Conclusion also describes the intervention as effective at changing health behavior. The paper does not establish the claim’s broader characterization of a solid body of evidence accumulated over two decades. (LLM, conf=0.86)
Experimental — accuracy not yet measuredBickmore et al reported that the group with only diet-related intervention consumed significantly more fruits and vegetables than groups which received only physical activity intervention or both physical activity and diet intervention (P=.005); however, there were no significant differences among different groups for weight (P=.37).
“Bickmore et al [33] reported that the group with only diet-related intervention consumed significantly more fruits and vegetables than the groups which received only physical activity intervention or both physical activity and diet intervention (P=.005); however, there were no significant differences among different groups for weight (P=.37).”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [33]
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Experimental — accuracy not yet measuredThe theory of motivational interviewing was used to devise interview questions addressed by NAO and motivation reinforcement messages provided by Bickmore et al's Chat1.
“The theory of motivational interviewing was also used to devise interview questions addressed by NAO [5] (the social robot) and the motivation reinforcement messages provided by Bickmore et al’s [33] Chat1.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [33]
The Introduction states that Bickmore et al.’s conversational-agent interventions drew on elements of motivational interviewing. It does not identify an agent called Chat1 or say that motivational interviewing was used to devise its motivation-reinforcement messages. The NAO portion is cited to a different source and is not assessed here. (LLM, conf=0.83)
Experimental — accuracy not yet measured80% (4/5) of studies reported an increase in physical activity.
“First, 80% (4/5) of studies [33] reported an increase in physical activity.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [33]
Bickmore et al reported no significant differences among conditions in the International Physical Activity Questionnaire (P=.37).
“However, only Bickmore et al [33] reported no significant differences among the conditions in the International Physical Activity Questionnaire (P=.37).”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [33]
20% (3/15) of studies reported an improvement in diet.
“Second, 20% (3/15) of studies [22,28,33] reported an improvement in diet.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [33]
Of the 15 studies, 7 (47%) targeted healthy lifestyles, and 5 (33%) assessed efficacy of AI chatbots in promoting healthy lifestyles.
“Out of the 15 studies, 7 (47%) studies [5,21,22,28,29,32,33] targeted healthy lifestyles, and 5 (33%) studies [21,22,28,32,33] assessed the efficacy of AI chatbots in promoting healthy lifestyles through (1) physical activity levels, (2) healthy diet, (3) blood pressure, and (4) BMI.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [33]
The findings add to the mixed evidence base on nutritional ECAs.
“Our findings might, on first sight, contradict our hypotheses, and add to the mixed evidence base on nutritional ECAs [5,6,44].”
The ECA shows particular promise for conveying behavioral change strategies to patients.
“One technology, called the embodied conversational agent (ECA), shows particular promise for conveying behavioral change strategies to patients [9, 12].”
From Engaging women with an embodied conversational agent to deliver mindfulness and lifestyle recommendations: A feasibility randomized control trial (2017) · cites this paper as [12]
Other studies have yielded positive outcomes for ECAs to deliver health education on topics such as exercise, eating fruits and vegetables, and preconception care.
“Other studies have also yielded positive outcomes for ECAs to deliver health education on topics such as: exercise, eating fruits and vegetables, and preconception care [12, 16, 21–24].”
From Engaging women with an embodied conversational agent to deliver mindfulness and lifestyle recommendations: A feasibility randomized control trial (2017) · cites this paper as [12]
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References 38 · Cited by 206
Research works printed but not linked: 2
- [3]Bouchard C Stephens T Physical activity and health: international proceedings and consensus statement 1994 Champaign, IL Human Kinetics
- [31]Thompson F Subar A Radimer K Performance of two new cognitively enhanced fruit and vegetable short assessment forms Proceedings of the Fourth International Conference on Dietary Assessment Methods 2000
- Authors
- Bickmore, T. W., Schulman, D., & Sidner, C.
- Affiliations
- Northeastern University (US)
- Worcester Polytechnic Institute (US)
- Corresponding author
- Bickmore, Timothy W.
- Venue
- Patient Education and Counseling
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 92
- Issue
- 2
- Pages
- 142-148
- ISSN
- 0738-3991 (ISSN-L)
- PMC
- PMC3727973
- OpenAlex
- W1997535568
- Cited by
- 206
- References
- 38
- Citations by year
- 2026: 13 · 2025: 23 · 2024: 24 · 2023: 18 · 2022: 23 · 2021: 21 · 2020: 16 · 2019: 21 · 2018: 13 · 2017: 8 · 2016: 8 · 2015: 4 · 2014: 10 · 2013: 3
- FWCI
- 15.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Patient Education and Counseling (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- U.S. National Library of Medicine (R21LM008995)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Keywords
- chatbots, physical activity, walking, fruit and vegetable intake, health behavior change, pedometers, computer-based intervention
- MeSH terms
- Adult, Aged, Artificial Intelligence, Computer-Assisted Instruction, Feeding Behavior, Female, Fruit, Health Promotion, Humans, Interviews as Topic, Male, Motor Activity, Outcome and Process Assessment, Health Care, Surveys and Questionnaires, Vegetables, Health Behavior, Walking, Qualitative Research
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