Predictors of EMA Compliance
EMA
As hypothesized, initial compliance was substantially higher for the morning assessments than for the random assessments (see Based on the present results and prior literature, compliance can be improved for random assessments by expanding the assessment window, using less stringent time-to-initiation/completion criteria, or incorporating a “snooze” feature for delaying initiating the survey [As predicted, the rate of decline in EMA compliance was negatively associated with participant age. Despite younger participants having higher rates of random assessment compliance at the start of the EMA period, compliance in the final week was approximately 20% higher for older participants (64 years old) than for younger participants (44 years old; see We initially conceptualized age as a predictor based on the perspective that compliance would be both higher initially and better maintained among participants with fewer competing demands and greater flexibility in their schedules (eg, retired participants [The most striking declines in EMA compliance (7%-10% per week) occurred among participants who eventually dropped out of the study. It is important to note that EMA compliance was coded as missing (not 0%) once a participant was no longer in the study. Consequently, declines in EMA compliance preceded study dropout. It seems plausible that either the burden of EMA contributed directly to study dropout or third variables (eg, general motivation or stress) contributed to both poor EMA adherence and study dropout. Either way, future studies may seek to replicate the relationship between study attrition and EMA compliance. Detection of early noncompliance or rapid declines in EMA compliance may be helpful for identifying participants who need extra support to be retained in long-term health behavior studies, a novel twist on using EMA to trigger just-in-time, adaptive interventions [Consistent with other smoking research, compliance was lower among African American participants than among White participants [Importantly, the predictors of EMA compliance discussed above (except for the COVID-19 context) all remained significant when included in a single, multiple predictor model, demonstrating that the effects discussed above were each uniquely and additively predictive of EMA compliance. However, because the effects are additive, it is also reasonable to consider them in combination. For example, the present findings suggest EMA compliance would be particularly low among younger African Americans who work full-time. Given the need to better capture the experiences of marginalized groups described above, these findings raise concerns about adequate representation in EMA data across individuals. This, in turn, raises the question: what constitutes “adequate” EMA compliance?