An Idiographic Examination of Day-to-Day Patterns of Substance Use Craving, Negative Affect, and Tobacco Use Among Young Adults in Recovery

Abstract

Psychological constructs, such as negative affect and substance use cravings that closely predict relapse, show substantial intra-individual day-to-day variability. This intra-individual variability of relevant psychological states combined with the “one day of a time” nature of sustained abstinence warrant a day-to-day investigation of substance use recovery. This study examines day-to-day associations among substance use cravings, negative affect, and tobacco use among 30 college students in 12-step recovery from drug and alcohol addictions. To account for individual variability in day-to-day process, it applies an idiographic approach. The sample of 20 males and 10 females (mean age = 21) was drawn from members of a collegiate recovery community at a large university. Data were collected with end-of-day data collections taking place over an average of 26.7 days. First-order vector autoregression models were fit to each individual predicting daily levels of substance use cravings, negative affect, and tobacco use from the same three variables one day prior. Individual model results demonstrated substantial inter-individual differences in intra-individual recovery process. Based on estimates from individual models, cluster analyses were used to group individuals into two homogeneous subgroups. Group comparisons demonstrate distinct patterns in the day-to-day associations among substance use cravings, negative affect, and tobacco use, suggesting the importance of idiographic approaches to recovery management and that the potential value of focusing on negative affect or tobacco use as prevention targets depends on idiosyncratic processes.

Illustrating idiographic methods for translation research: moderation effects, natural clinical experiments, and complex treatment-by-subgroup interactions

“…Table 2 presents fit statistics for (1) wholesample-aggregated estimates versus (2) treatmentspecific parameters (the latter better fit observed data). A third, exploratory, model (3) tested the fit of subgroups of participants based on the technique of Zheng et al for K-cluster analysis of their model parameters [13]. Four ORIGINAL RESEARCH clusters emerged, summarized in the first, seventh, and eighth columns of Table 1.…”

“…As with RCTs, ICTs might identify subgroups of clients/patients but do so by identifying similarities among the individuals (e.g., homogeneous clusters) in terms of trends of outcomes or related characteristics, in a bottom-up manner [13]. Population inferences are usually not the purpose of an ICT.…”

“…Unified structural equation modeling (USEM) is a form of state-space modeling which in some ways resembles SEM but models observation-toobservation changes (e.g., day-to-day or session-to-session) within individuals [13]. Chow et al [11] review consistencies and differences between USEM and SEM including their relative strengths and weaknesses with regard to investigating intraindividual change and interindividual variability.…”

“…For example, the fit statistics used herein for state-space model testing have established literatures and examples in the literature within time series data and ICT contexts [11,13]. Yet, alternative statistics may be more appropriate, offer greater power (in the case of LR χ 2 ), or provide requisite conservative adjustments of which the field is not yet aware.…”

“…Cross-lag model mediations between relationship satisfaction and depression were analyzed within individuals (see Fig. 2, later) [13,22]. Herein, MMTA was conducted twice: first for outcomes as treatment was administered and again with 12-month outcomes added to explore differences between their results [23].…”