Background Although 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 engage in physical activity. The literature on behavior change techniques (BCTs) and self-determination theory (SDT) contains promising insights that can be leveraged in the design of these technologies; however, it remains unclear how this can be achieved. Objective This study aimed to evaluate the feasibility of a chatbot system that improves the user’s autonomous motivation for walking based on BCTs and SDT. First, we aimed to develop and evaluate various versions of a chatbot system based on promising BCTs. Second, we aimed to evaluate whether the use of the system improves the autonomous motivation for walking and the associated factors of need satisfaction. Third, we explored the support for the theoretical mechanism and effectiveness of various BCT implementations. Methods We developed a chatbot system using the mobile apps Telegram (Telegram Messenger Inc) and Google Fit (Google LLC). We implemented 12 versions of this system, which differed in 3 BCTs: goal setting, experimenting, and action planning. We then conducted a feasibility study with 102 participants who used this system over the course of 3 weeks, by conversing with a chatbot and completing questionnaires, capturing their perceived app support, need satisfaction, physical activity levels, and motivation. Results The use of the chatbot systems was satisfactory, and on average, its users reported increases in autonomous motivation for walking. The dropout rate was low. Although approximately half of the participants indicated that they would have preferred to interact with a human instead of the chatbot, 46.1% (47/102) of the participants stated that the chatbot helped them become more active, and 42.2% (43/102) of the participants decided to continue using the chatbot for an additional week. Furthermore, the majority thought that a more advanced chatbot could be very helpful. The motivation was associated with the satisfaction of the needs of competence and autonomy, and need satisfaction, in turn, was associated with the perceived system support, providing support for SDT underpinnings. However, no substantial differences were found across different BCT implementations. Conclusions The results provide evidence that chatbot systems are a feasible means to increase autonomous motivation for physical activity. We found support for SDT as a basis for the design, laying a foundation for larger studies to confirm the effectiveness of the selected BCTs within chatbot systems, explore a wider range of BCTs, and help the development of guidelines for the design of interactive technology that helps users achieve long-term health benefits.

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 suggests that for an effective design of such interactions, adopting Behavioural Change Techniques (BCTs) based on Self-Determination Theory (SDT) seems promising, but this remains untested. OBJECTIVE The objectives of our study are (1) to test whether autonomous motivation for walking can be increased when a chatbot in combination with an activity tracking smartphone application (app) is used, (2) to confirm the underlying theoretical mechanisms, and (3) to evaluate the effectiveness of various BCT implementations. METHODS We employed a 2x2x3 factorial field experiment, using 12 variations of a chatbot which differed in three BCTs: goal setting, experimenting, and action planning. In total, 102 participants used a variation of the chatbot together with the Google Fit app over the course of three weeks. Each week, participants were asked to have a conversation with the chatbot and to complete a questionnaire capturing their perceived app/chatbot support, need-satisfaction, and physical activity levels. Motivation was measured before and after the three-week period. RESULTS On average, across all variations of the chatbot, participants reported significant increases in autonomous motivation (P<.001). Motivation was associated with need-satisfaction (P<.001) and need-satisfaction was associated with perceived app/chatbot support (P=.002). In terms of the different BCT implementations no significant differences were found. While many participants (49%) 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. CONCLUSIONS The results provide evidence that a chatbot in combination with a physical activity tracking app such as Google Fit can increase autonomous motivation by supporting the needs of competence and autonomy. Our study also clarifies a need to further study how the corresponding Behavioural Change Techniques are best implemented, and how other BCTs could be studied.