The association between depression and digital media use (DMU) has received substantial research and popular attention in recent years. While meta-analytic evidence indicates that there is a small, positive relationship between DMU and depression, almost all studies rely on self-report measures of DMU. Evidence suggests these measures are poor reflections of usage measures derived from digital trace data. Additionally, a recent study showed that the error in self-reported DMU is likely biased systematically by factors that are fundamental to the effect being investigated: respondents’ volume of use and level of depression. The current study harnesses cubic response surface analysis—a novel analytical approach in this domain—to advance our understanding of how inaccuracies in self-report measures of DMU can be explained by respondent attributes, in this case their level of depression and actual iPhone usage. A sample of 325 iPhone users provided estimates of their total iPhone use over the past week, their actual iPhone use as recorded by the Apple Screen Time application, and a measure of their depression (CESD-R-10). The results of the analysis indicate that depression is i.) more strongly associated with estimated than device-logged DMU; ii.) more associated with over-estimating than under-estimating of DMU; and iii.) more associated with inaccuracy at lower versus higher levels of DMU. The findings raise important questions concerning the validity of conclusions in this area and provide insight into the structure of measurement error in self-report estimates of DMU.