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. 2022 Feb;45(1):50-61.
doi: 10.1007/s10865-021-00248-4. Epub 2021 Aug 6.

Stable and momentary psychosocial correlates of everyday smoking: An application of Temporal Self-Regulation Theory

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Stable and momentary psychosocial correlates of everyday smoking: An application of Temporal Self-Regulation Theory

Christopher M Jones et al. J Behav Med. 2022 Feb.

Abstract

Smoking is one of the leading causes of non-communicable disease mortality and morbidity. Smoking behaviour is determined by both stable, person-level (e.g., motivation, nicotine dependence) and variable, situation-level factors (e.g., urges, cues). However, most theoretical approaches to understanding health behaviours so far have not integrated these two spheres of influence. Temporal Self-Regulation Theory (TST) integrates these person-level and situation-level factors, but has not yet been comprehensively applied to predicting smoking behaviour. We use Ecological Momentary Assessment to examine the utility of TST in predicting daily smoking. 46 smokers reported individual and environmental cues right after smoking and at random time points during the day. Cognitions, self-control, past behaviour, and nicotine dependence were assessed at baseline. Multi-level logistic regressions show that smoking is largely guided by momentary cues, but individual motivation can buffer their influence. This suggests that TST is a useful integrative approach to understand modifiable determinants of smoking and thus intervention targets.

Keywords: Ecological Momentary Assessment; Health behaviour; Self-regulation; Smoking; Temporal Self-Regulation Theory.

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Conflict of interest statement

All authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Conceptual diagram for repetitive behaviours in a supportive (a) and non-supportive ecological context (b), adapted from Hall and Fong (2007). Broken arrows denote weaker hypothesized effects
Fig. 2
Fig. 2
Model-based predicted probabilities of type of prompt as a function of whether others are smoking in view (levels: “no”, coded as − 1; “yes”, coded as 1) and momentary intentions to quit (Lüdecke, 2018). Shaded intervals depict the 95% confidence intervals around model predictions

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