Dataset Information

Network analysis reveals the associations of past quit experiences on current smoking behavior and motivation to quit.

ABSTRACT:

Introduction

Smoking is a leading cause of morbidity and mortality in the United States. While most smokers endorse a desire to quit, achieving abstinence is notoriously difficult. Network analysis is a method for understanding the complex relationships of factors that maintain smoking behavior and impact motivation to quit.

Methods

This study examined self-report prescreen data from treatment-seeking smokers (N = 3913). The number of prior quit attempts and withdrawal symptoms experienced, as well as current smoking behavior and motivation to quit were modeled as interconnected nodes in a network. Two key network metrics were examined: 1) edge weights, which quantify the strength and direction of the associations of interest, and 2) the sum of each node's edge weights, which quantifies the expected influence of a node on the overall network.

Results

The withdrawal symptom of craving, r = 0.10, 95% CI [0.07, 0.13] and digestive problems, r = -0.06, 95% CI [-0.09, -0.03], had the strongest positive and negative association with daily cigarettes, respectively. The number of prior quit attempts, r = 0.17, 95% CI [0.14, 0.20], concentration problems, r = -0.04, 95% CI [-0.027, -0.01], showed the strongest positive and negative associations, respectively, with current motivation to quit. Nodes with significant links to current smoking and motivation to quit were also among the most influential in the overall network.

Conclusions

Findings suggest prior quit experiences and consequences associated with withdrawal symptoms may differentially relate to maintenance of smoking behavior and motivation to quit in treatment-seeking smokers. Interventions targeting key withdrawal symptoms may enhance motivation to quit.

SUBMITTER: Dutcher CD

PROVIDER: S-EPMC7736500 | biostudies-literature |

REPOSITORIES: biostudies-literature

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