Because it is difficult to objectively measure population-level physical activity levels, self-reported measures have been used as a surveillance tool. However, little is known about their validity in populations living in dense urban areas. We aimed to assess the validity of self-reported physical activity data against accelerometer-based measurements among adults living in New York City and to apply a practical tool to adjust for measurement error in complex sample data using a regression calibration method. We used 2 components of data: 1) dual-frame random digit dialing telephone survey data from 3,806 adults in 2010–2011 and 2) accelerometer data from a subsample of 679 survey participants. Self-reported physical activity levels were measured using a version of the Global Physical Activity Questionnaire, whereas data on weekly moderate-equivalent minutes of activity were collected using accelerometers. Two self-reported health measures (obesity and diabetes) were included as outcomes. Participants with higher accelerometer values were more likely to underreport the actual levels. (Accelerometer values were considered to be the reference values.) After correcting for measurement errors, we found that associations between outcomes and physical activity levels were substantially deattenuated. Despite difficulties in accurately monitoring physical activity levels in dense urban areas using self-reported data, our findings show the importance of performing a well-designed validation study because it allows for understanding and correcting measurement errors.