Smoking tobacco and physical inactivity are key preventable behavioural risk factors of cardiovascular disease (CVD). Computerised coaching systems can help individuals to modify risky behaviours, thereby preventing CVD. However, most reported eHealth or computerized coaching systems are hard to reuse in slightly different settings. To provide an open-source, reusable computer coaching system, we developed Perfect Fit. The reusability is manifested by building around the open-source text- and voice-based contextual assistant framework Rasa. Rasa provides a simple, standard interface to many popular messaging and voice channels, and custom connectors are easily implemented. A set of algorithms have been developed and connected to Rasa to drive and personalize the conversation flow and the coaching process. Such algorithms make use of data stored in a devoted database. Furthermore, Perfect Fit adheres to best practices and standards in software engineering. The modular design of Perfect Fit will allow researchers to connect the virtual coach to any messaging or voice channel with only modest modification. Perfect Fit is available under open-source license in GitHub and is currently in prototype-phase. Concluding, Perfect Fit will deliver a virtual coach that can easily be adapted and reused in different settings. The coach helps individuals to achieve and maintain abstinence from smoking and sufficient physical activity (PA).@en

Background: Smoking and physical inactivity are two key preventable risk factors of cardiovascular disease. Yet, as with most health behaviors, they are difficult to change. In the interdisciplinary Perfect Fit project, scientists from different fields join forces to develop an evidence-based virtual coach that supports smokers in quitting smoking and increasing their physical activity. Intervention content, design and implementation as well as lessons learnt are presented in the hopes of guiding other research groups working on similar projects. Methods: Numerous approaches were used and combined to support the development of the Perfect Fit virtual coach. Approaches include literature reviews, empirical studies, collaboration with end-users, content and technical development sprints, interdisciplinary collaboration and iterative proof-of-concept implementation. Findings: The Perfect Fit intervention integrates evidence-based behavioral change techniques as well as new techniques focused on identity change, big data science, sensor technology and personalized real-time coaching. Intervention content of the virtual coaching matches communication preferences and individual needs of end users. Lessons learnt include ways to optimally implement and tailor interactions from the virtual coach (e.g., ‘explain why user is asked for input’, ‘tailor timing and frequency of intervention components’). With regards to the development process, lessons learnt include strategies for effective interdisciplinary collaboration and technical development (e.g., ‘Find a good balance between wishes of end-users and legal possibilities’). Discussion: The Perfect Fit development process was interactive, iterative and challenging at times. We hope that our experiences and lessons learnt can inspire and benefit others.@en