Introducing a Real-Time Method for Identifying the Predictors of Noncompliance with Event-Based Reporting of Tobacco Use in Ecological Momentary Assessment

Abstract Background Little is known about the factors that bias event-based (i.e., self-initiated) reporting of health behaviors in ecological momentary assessment (EMA) due to the difficulty inherent to tracking failures to self-initiate reports. Purpose To introduce a real-time method for identifying the predictors of noncompliance with event-based reporting. Methods N = 410 adults who used both cigarettes and e-cigarettes completed a 1-week EMA protocol that combined random reporting of current contexts with event-based reporting of tobacco use. Each random assessment first asked if participants were currently using tobacco and, if so, the assessment converted into a “randomly captured” event report—indicating failure to self-initiate that report. Multilevel modeling tested predictors of failing to complete random reports and failing to self-initiate event reports. Results On the person level, male sex, higher average cigarette rate, and higher average cigarette urge each predicted missing random reports. The person-level predictors of failing to self-initiate event reports were older age, higher average cigarette and e-cigarette rates, higher average cigarette urge, and being alone more on average; the moment-level predictors were lower cigarette urge, lower positive affect, alcohol use, and cannabis use. Strikingly, the randomly captured events comprised more of the total EMA reports (28%) than did the self-initiated event reports (24%). These report types were similar across most variables, with some exceptions, such as momentary cannabis use predicting the random capture of tobacco events. Conclusions This study demonstrated a method of identifying predictors of noncompliance with event-based reporting of tobacco use and enhancing the real-time capture of events. This study demonstrated a real-time method for identifying person- and moment-level predictors of failing to self-initiate tobacco event reports during ecological momentary assessment (EMA), and for capturing a large number of events that would have likely otherwise been missed. Lay Summary This study introduced a real-time method for identifying person- and moment-level predictors of failing to self-initiate tobacco event reports during ecological momentary assessment (EMA), and for capturing a large number of events that would have likely otherwise been missed. The method has implications for behavioral health research more broadly.