Pushrim-activated power-assisted wheel (PAPAW) users ideally require different levels of assistance depending on activity and preference. Therefore, it is important to design and develop adaptive PAPAW controllers to account for these differences. The main objective of this work was to integrate a user intention estimation framework into a PAPAW and develop personalized adaptive controllers. We performed experiments to gather kinetics of wheelchair propulsion for a variety of daily life wheelchair activities. The propulsion characteristics (i.e., pushrim forces) were used to train intention estimation models and characterize implicit user intentions when performing daily life wheelchair maneuvers. These intentions included moving straight forward, performing a right/left turn, and braking. The intention estimation framework, based on random forest classification algorithms and kinetic features, was implemented and tested in our laboratory-developed PAPAW. This computationally efficient framework was successfully implemented and tested for each participant in real-time. Our results revealed that the real-time user intention predictions were similar to the offline models. The power-assist ratio of each wheel was adjusted based on which user intention was identified. Data collected from four participants provided evidence regarding the effectiveness of using adaptive intention-based controllers. For instance, the propulsion effort was significantly reduced when using an adaptive PAPAW controller. Subjective views of participants regarding the workload of wheelchair propulsion (e.g., physical/cognitive effort) were also gathered. Our findings suggest that rankings of different controllers varied among different participants and across different wheelchair maneuvers, indicating the need for customized adaptive controllers to fit different users' activities and preferences.