Truth and memory: Linking instantaneous and retrospective self-reported cigarette consumption

TL;DR

This study compares real-time electronic diary data with retrospective self-reports of cigarette use, modeling biases in recall and rounding to improve understanding of smoking behavior measurement.

Contribution

It introduces a Bayesian statistical model that quantifies mis-remembering and heaping effects in self-reported cigarette consumption data.

Findings

Both mis-remembering and rounding significantly distort self-reported data.

Higher nicotine dependence, white ethnicity, and male sex are linked to greater recall of smoking.

The model effectively captures the processes behind self-report biases.

Abstract

Studies of smoking behavior commonly use the time-line follow-back (TLFB) method, or periodic retrospective recall, to gather data on daily cigarette consumption. TLFB is considered adequate for identifying periods of abstinence and lapse but not for measurement of daily cigarette consumption, thanks to substantial recall and digit preference biases. With the development of the hand-held electronic diary (ED), it has become possible to collect cigarette consumption data using ecological momentary assessment (EMA), or the instantaneous recording of each cigarette as it is smoked. EMA data, because they do not rely on retrospective recall, are thought to more accurately measure cigarette consumption. In this article we present an analysis of consumption data collected simultaneously by both methods from 236 active smokers in the pre-quit phase of a smoking cessation study. We define a statistical model that describes the genesis of the TLFB records as a two-stage process of mis-remembering and rounding, including fixed and random effects at each stage. We use Bayesian methods to estimate the model, and we evaluate its adequacy by studying histograms of imputed values of the latent remembered cigarette count. Our analysis suggests that both mis-remembering and heaping contribute substantially to the distortion of self-reported cigarette counts. Higher nicotine dependence, white ethnicity and male sex are associated with greater remembered smoking given the EMA count. The model is potentially useful in other applications where it is desirable to understand the process by which subjects remember and report true observations.