Persuading to Prepare for Quitting Smoking with a Virtual Coach

Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior

Albers, N. (TU Delft Interactive Intelligence)
Neerincx, M.A. (TU Delft Interactive Intelligence; TNO)
Brinkman, W.P. (TU Delft Interactive Intelligence)

2023

Despite their prevalence in eHealth applications for behavior change, persuasive messages tend to have small effects on behavior. Conditions or states (e.g., confidence, knowledge, motivation) and characteristics (e.g., gender, age, personality) of persuadees are two promising components for more effective algorithms for choosing persuasive messages. However, it is not yet sufficiently clear how well considering these components allows one to predict behavior after persuasive attempts, especially in the long run. Since collecting data for many algorithm components is costly and places a burden on users, a better understanding of the impact of individual components in practice is welcome. This can help to make an informed decision on which components to use. We thus conducted a longitudinal study in which a virtual coach persuaded 671 daily smokers to do preparatory activities for quitting smoking and becoming more physically active, such as envisioning one’s desired future self. Based on the collected data, we designed a Reinforcement Learning (RL)-approach that considers current and future states to maximize the effort people spend on their activities. Using this RL-approach, we found, based on leave-one-out cross-validation, that considering states helps to predict both behavior and future states. User characteristics and especially involvement in the activities, on the other hand, only help to predict behavior if used in combination with states rather than alone. We see these results as supporting the use of states and involvement in persuasion algorithms. Our dataset is available online.

Persuasion Algorithm
Reinforcement Learning
Conversational Agent
eHealth
Smoking
Behavior Change
Physical Activity

http://resolver.tudelft.nl/uuid:dca912f7-44a7-4a48-891a-94e9a6e0358e

International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), Richland

2023-11-30

978-1-4503-9432-1

AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems

2023 International Conference on Autonomous Agents and Multiagent Systems, 2023-05-29 → 2023-06-02, London, United Kingdom

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 N. Albers, M.A. Neerincx, W.P. Brinkman