Widespread use of Internet and mobile technologies provides opportunities to gather health-related information to complement data generated through traditional healthcare and public health systems. These personally generated data (PGD) are increasingly viewed as informative of the patient experience of conditions, symptoms, treatments, and side effects. Behavior, sentiment, and disease patterns can be discerned from mining unstructured PGD in text, image, or metadata form, and from analyzing PGD collected via structured, opt-in, and web-enabled platforms and devices, including wearables. Models that employ PGD from distributed cohorts are being used increasingly to measure public health outcomes; moreover, PGD collection forms the centerpiece of important new federal investments into personalized medicine that seek to energize vast cohorts in donating data via apps and devices. PGD offer the opportunity to inform gap areas of health research through high-resolution views into spatial, temporal, or demographic features. However, when PGD are used to answer epidemiologic questions, it is not always clear what constitutes the population at risk (PAR), or the denominator, challenging researchers’ abilities to make inferences, draw comparisons, and evaluate change. Because of this, initial PGD studies have tended toward numerator-only investigations; however, the field is advancing. This report summarizes issues related to specifying PAR and denominator metrics when using PGD for health research and outlines approaches for resolving these issues using design and analytic strategies.