Compliance and Response Consistency in a Lengthy Intensive Longitudinal Data Protocol

Research on real-world patterns of substance use increasingly involves intensive longitudinal data (ILD) collection, requiring long assessment windows. The present study extends limited prior research examining event- and person-level influences on compliance and response consistency by investigating how these behaviors are sustained over time in an ILD study of alcohol and cannabis co-use in college students. Participants (n = 316) completed two 28-day bursts of ILD comprising five daily surveys, which included a morning survey of prior-day drinking. We used linear mixed effects models in a multilevel interrupted time series framework to evaluate the associations of time and measurement burst with (a) noncompliance (count of missed surveys) and (b) response consistency (difference between same-day report of drinking and morning report of prior-day drinking). We observed that time was positively associated with noncompliance, with no discontinuity associated with measurement burst. The slope of time was more positive in the second burst. Neither time nor measurement burst were significantly associated with consistent reporting. However, survey nonresponse and consistency of responding appeared to be impacted by the same-day use of substances. Overall, compliance decreased while consistency was stable across the duration of a lengthy ILD protocol. Shorter assessment windows or adaptive prompting strategies may improve overall study compliance. Further work examining daily burden and context is needed to inform future ILD design. Public Significance Statement This study examined compliance and consistency of reporting in a lengthy, intensive longitudinal data protocol spanning 56 days of data collection across two measurement bursts. Results showed that compliance decreased over time, with no change associated with the discontinuity between bursts, while consistency remained constant and of moderate magnitude. Findings inform the design and handling of missing data in future intensive longitudinal research.