Pooled Time Series Modeling Reveals Smoking Habit Memory Pattern.

Jesús F Rosel,Sara Puchol,Francisco H Machancoses,Juan J Canales,Patricia Flor-Arasil,Marcel Elipe-Miravet,Jacinto Pallarés,Eduardo Elósegui

doi:10.3389/fpsyt.2020.00049

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.

Highlights

Patterned behavior can be predicted on the basis of the behavior displayed in the immediate, or even remote, past
The results obtained in this research confirm the main hypotheses of the proposed model, this is, that the smoking habit has an autoregressive process (AR) component, of 56 days, and that the day of the week leads to a change in the number of cigarettes smoked
As stated in the introduction, previous studies found that participants followed AR models with a maximum of 14 days

Summary

Introduction

Patterned behavior can be predicted on the basis of the behavior displayed in the immediate, or even remote, past. If a temporary process was measured in days, and the maximum delay to accurately forecast the series was five lags, it would follow that the memory of Modeling Smoking Habit Memory Pattern the series, this is, the autoregressive process (AR), is 5 days (AR [5]). This statistical concept is increasingly used in time series as applied in physical, social, engineering, and statistical sciences [5,6,7,8]. Our aim here is to temporally define the smoking habit memory pattern, understood as a statistical modeling of habitual smoking, and determine the maximum delay (behavioral memory) of the model

Methods

Results

Conclusion