## 1 Introduction and Background
's [20] selMA chatbot increased positive intentions toward behavior change but did not yield statistically significant improvements in disability outcomes. [PERSON] et al. [21] demonstrated that Zemedy, a chatbot for irritable bowel syndrome, effectively improved symptoms, quality of life, and psychological outcomes. These findings underscore the versatility and effectiveness of CAs in managing chronic diseases, offering personalized, scalable, and impactful care while highlighting the importance of addressing challenges such as user engagement, ethical considerations, and seamless integration into healthcare systems. By integrating CAs into mobile apps, patients can receive reminders, motivational messages, and educational content related to their treatment plans in real time. This real-time communication helps patients adhere to their therapy regimens and increases accessibility, particularly for rural or remote areas with limited access to medical professionals [14]. AI-powered CAs offer significant benefits for patient self-management by providing smarter features such as personalized interventions, lifestyle recommendations, and support for treatment engagement [22]. Cloud-based solutions such as IBM watsonx Assistant, Amazon Lex, Google Cloud Dialogflow, and Azure AI Bot facilitate chatbot implementation. However, the selection of chatbot services must consider language capabilities, as natural language processing techniques are employed to interpret requests, and not all services support all languages. Moreover, motivational models provide a framework for understanding how people are motivated and what drives their behavior [23]. According to [PERSON] et al. [24], motivation is a key factor because it influences people's ability to use, learn, and make decisions, among other things. Recently, several studies have shown that motivation plays an important role in patient engagement in rehabilitation and is a predictor of functional outcomes. While the role of motivation in driving behavior change is well-established, the application of motivational models and strategies in neurorehabilitation research has only recently begun to be systematically explored and described [25; 26]. Neglecting the design of motivational models, including how motivation is employed when designing mHealth systems, can lead to a design that discourages users from using it. By integrating motivational models into the CA design, developers can tailor messages and interactions to the specific needs and preferences of users, increasing their motivation to engage with the agent and move forward with recommended behaviors. This paper details the design and development of PANDORA (Parkinson Assistant in Natural Dialogue and Oriented by Rules and Assessments), an AI-driven CA integrated into the ONParkinson mobile app, which seeks to motivate and support PwPD in their exercise routines. PANDORA is based on the principles of Self-Determination Theory (SDT) [27], a well-established motivational framework that emphasizes three core psychological needs--autonomy, competence, and relatedness--as essential drivers of sustained behavior change. By addressing these needs, PANDORA aims to empower users to take an active role in managing their health, fostering a sense of control and connection that is often lacking in traditional therapeutic approaches. This study outlines the development process of PANDORA, including its technical performance evaluation and user experience assessment, and explores its potential to improve exercise adherence in PwPD. The findings presented here aim to contribute to the growing field of AI-driven mHealth applications, particularly those focused on enhancing therapeutic engagement and self-management in chronic conditions like [PERSON]'s disease.