Smoking is one of the most challenging behavioral health problems. In the past, failed quit attempts have been attributed to factors including stress, presence of smoking cues, and negative affect-most of which were self-reported and prone to recall-bias. The first step in designing effective smoking cessation systems is to objectively identify factors that contribute to lapse. In our research, we collected physiological data utilizing wearable sensors from a four day pre-quit, post-quit study (N=55). We also collected self-report measures (n=3120), which offer rich contextual information about users' social, emotional, geographical, and physiological conditions. Analysis of collected data informed the design of MyQuitPal, a participant-centric cessation support system, which aims to assist individuals to better understand their smoking behavior. The design of MyQuitPal is also grounded on theories of long term health-behavior change. We believe our research advances understanding of complexities and opportunities surrounding the design of smoking cessation systems.