Data collected by multiple physiological sensors are being increasingly used for wellness monitoring or disease management, within a pervasiveness context facilitated by the massive use of mobile devices. These abundant complementary raw data are challenging to understand and process, because of their voluminous and heterogeneous nature, as well as the data quality issues that could impede their utilization. This chapter examines the main data quality questions concerning six frequently used physiological sensors - glucometer, scale, blood pressure me-ter, heart rate meter, pedometer, and thermometer -, as well as patient observations that may be associated to a given set of measurements. We discuss specific details that are either overlooked in the literature or avoided by data exploration and information extraction algorithms, but have significant importance to properly pre-process these data. Making use of different types of formalized knowledge, according to the characteristics of physiological measurement devices, relevant data handled by a Personal Health Record on a mobile device, are evaluated from a data quality perspective, considering data deficiencies factors, consequences and reasons. We propose a general scheme for sensors data quality characterization adapted to a pervasive scenario.