Supporting Autonomous Motivation for Physical Activity with Chatbots during COVID-19: A Factorial Experiment (Preprint)

BACKGROUND While physical activity can mitigate disease trajectories, and improve and sustain mental health, many people have become less physically active during the COVID-19 pandemic. Personal information technology, such as activity trackers and chatbots, can technically converse with people and possibly enhance their autonomous motivation to undertake physical activity. The literature on Behavioural Change Techniques (BCTs) and Self-Determination Theory (SDT) contains promising insights that can be leveraged in the design of these technologies, but it remains unclear how this can be achieved. OBJECTIVE The overall objective of our study is to evaluate the feasibility of a chatbot system that improves the user’s autonomous motivation for walking, based on BCTs and SDT. First, we aim to develop and evaluate various versions of a chatbot system, based on promising BCTs. Second, we aim to evaluate whether the use of the system improves the autonomous motivation for walking and its factors of need satisfaction. Third, we explore support for the theoretical mechanism and the effectiveness of various BCT implementations. METHODS We developed a chatbot system using the mobile apps Telegram and Google Fit. We implemented 12 versions of this system, differing in three BCTs: goal setting, experimenting, and action planning. We then conducted a feasibility study with 102 participants using this system over the course of three weeks. Each week, participants were asked to converse with the chatbot and to complete a questionnaire capturing their perceived app/chatbot support, need-satisfaction, and physical activity levels. Motivation and physical activity levels were measured before and after the three-week period. RESULTS The use of the chatbot systems was satisfactory, and on average, its users reported increases in autonomous motivation for walking. The rate of dropouts was low. While about half of participants would have preferred to interact with a human instead of the chatbot, 46% of the participants stated that the chatbot helped them to become more active, and 42% of the participants decided to keep using the chatbot for an additional week. Furthermore, a majority thought that a more advanced chatbot could be very helpful. Motivation was associated with need-satisfaction of competence and autonomy, and need-satisfaction was associated with perceived system support, providing support for the SDT underpinnings. However, no significant differences were found across different BCT implementations. CONCLUSIONS The results provide evidence that a chatbot system is a feasible means to increase autonomous motivation for physical activity. We found support for Self-Determination Theory as a basis for the design, laying a foundation for larger studies to confirm the effectiveness of the selected BCTs within chatbot systems, and to explore a wider range of BCTs, toward the design guidelines of interactive technology that help users achieve long term health benefits.