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

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.

The Importance of Filter Collection for Accurate Measurement of Cigarette Smoking

“…Digit bias or, heaping, which is a clustering of reported estimates around rounded values 6–8 is common and has been well documented in the literature. 1,9–15 …”

“…Heaping in self-reports often occurs in multiples of 5 or 10 and is not cigarette specific. 14,15,28–30 For cigarette smokers, heaping at 10 or 20 (half-pack or full-pack of cigarettes) may impact the assessment of nicotine dependence, specifically with the Fagerström Test for Nicotine Dependence (FTND) because cut-points of this measure occur in counts of 10 (FTND categories: 10 or less, 11–20, 21–30, 31+). Heaping in self-reports, that differs from actual consumption, even by a few cigarettes, can lead to incorrect FTND classification.…”

Accuracy of the Smoking Questionnaire

“…The test-retest reliability of daily cigarette consumption displayed a U-shaped pattern over time, with best agreement found for periods 20 or more years in the past. The tendency of reporting the smoking of multiples of 5 or 10 cigarettes per day (heaping) could lead to underestimating within-subject variability (16) and can artificially increase the percent agreement of the reported consumption, progressively over the period of 10 or more years in the past. The phenomenon coincides with the gradual reorganization of cognitions from episodic to semantic representation, comprising a partial loss of the capacity to remember particular events and details, and the abstraction of gist memories (17).…”

Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth–death processes

“…Wang and Heitjan (2008) discuss validation of reported counts of cigarettes smoked by measuring tobacco products in the blood. In related work, Wang et al (2012) compare instantaneous and retrospective self-reports of cigarette consumption under a similar model for heaping. Other survey methods are possible, including using diary-like surveys or repeated questionnaires to assess reporting error.…”

“…Many approaches characterize the reporting mechanism as a choice between reporting truthfully and misreporting at suspected heaping grid points (for example, Wang and Heitjan, 2008; Wright and Bray, 2003; Wang et al, 2012; Bar and Lillard, 2012). The probability of reporting a particular heaped value depends on the value of the latent true value: Wang and Heitjan (2008) use a proportional odds model for different heaping grids; Bar and Lillard (2012) propose a multinomial distribution governing the choice of different heaping rules; McLain et al (2014) propose a semi-parametric model for heaping (digit preference) of duration-time data in which subjects are equally likely to round up or down.…”