This dissertation research investigates the social implications of computing artifacts that make use of sensor driven self-quantification to implicitly or explicitly direct user behaviors. These technologies are referred to here as self-sensoring prescriptive applications (SSPA’s). This genre of technological application has a strong presence in healthcare as a means to monitor health, modify behavior, improve health outcomes, and reduce medical costs. However, the commercial sector is quickly adopting SSPA’s as a means to monitor and/or modify consumer behaviors as well (Swan, 2013). These wearable devices typically monitor factors such as movement, heartrate, and respiration; ostensibly to guide the users to better or more informed choices about their physical fitness (Lee & Drake, 2013; Swan, 2012b). However, applications that claim to use biosensor data to assist in mood maintenance and control are entering the market (Bolluyt, 2015), and applications to aid in decision making about consumer products are on the horizon as well (Swan, 2012b). Interestingly, there is little existing research that investigates the direct impact biosensor data have on decision making, nor on the risks, benefits, or regulation of such technologies. The research presented here is inspired by a number of separate but related gaps in existing literature about the social implications of SSPA’s. First, how SSPA’s impact individual and group decision making and attitude formation within non-medical-care domains (e.g. will a message about what product to buy be more persuasive if it claims to have based the recommendation on your biometric information?). Second, how the design and designers of SSPA’s shape social behaviors and third, how these factors are or are not being considered in future design and public policy decisions.