Pooled Time Series Modeling Reveals Smoking Habit Memory Pattern

Abstract

Smoking is a habit that is hard to break because nicotine is highly addictive and smoking behavior is strongly linked to multiple daily activities and routines. Here, we explored the effect of gender, age, day of the week, and previous smoking on the number of cigarettes smoked on any given day. Data consisted of daily records of the number of cigarettes participants smoked over an average period of 84 days. The sample included smokers (36 men and 26 women), aged between 18 and 26 years, who smoked at least five cigarettes a day and had smoked for at least 2 years. A panel data analysis was performed by way of multilevel pooled time series modeling. Smoking on any given day was a function of the number of cigarettes smoked on the previous day, and 2, 7, 14, 21, 28, 35, 42, 49, and 56 days previously, and the day of the week. Neither gender nor age influenced this pattern, with no multilevel effects being detected, thus the behavior of all participants fitted the same smoking model. These novel findings show empirically that smoking behavior is governed by firmly established temporal dependence patterns and inform temporal parameters for the rational design of smoking cessation programs.