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.

Longitudinal Effects of Distress and Its Management During COVID-19 Lockdown in Spain

“…In addition, computerized registration systems allow almost continuous data collection, as in the present investigation. Different variables have been collected over multiple days belonging to different individuals, which requires data analysis models appropriate to the system used, so it is expected that training in data-intensive analysis models using pooled time series and other techniques (multilevel analysis, resampling,…) will increase ( Nusser et al, 2006 ; Bolger and Laurenceau, 2013 ; Walls, 2013 ; Rosel et al, 2020 ). So, future studies on the temporal processes of Distress should be carried out to check if its memory and the influential independent variables on Distress are similar during confinement and under non-confinement conditions, as in the “de-escalation” of lockdown limitations, and in periods of normality.…”

Toward AI-Guided Smoking Cessation: Individualized Nicotine Addiction Modeling Using Gaussian Processes

“…We observe that weekly patterns emerge in smokers’ nicotine use [17]. Thus, we can formulate the problem as periodical time forecasting, for which GPs have proven to be effective [18].…”

“…We utilize the dataset provided in [17], which contains a sample of 62 participants who have been smoking for more than two consecutive years and who smoke more than five cigarettes a day. Participants were male and female college students between 18 and 26 years of age.…”

Estudio de la memoria conductual de sustancias adictivas mediante análisis de series temporales agrupadas

“…En conclusión, si se usan métodos de análisis transversales con datos longitudinales, los valores de las pruebas t, z, R 2 o F van a ser más elevados si se da la condición de que los residuos de las variables están correlacionados entre ellos, cosa que no puede acaecer con los datos en panel (Box et al, 2015;Rosel et al, 2019Rosel et al, , 2020. Si se diera este hecho, aumentaría la probabilidad de que ocurrieran errores de tipo α, es decir, se rechazaría la hipótesis nula cuando se tendría que aceptar.…”

“…Como se ha mencionado, la conducta humana sigue un patrón habitual, es decir, siguen un comportamiento rutinario. Esto es especialmente cierto cuando se llevan a cabo ciertas conductas recurrentes, como podría ser el consumo de ciertas sustancias adictivas (Rosel et al, 2020;Velicer & Molenaar, 2013). Por lo tanto, queda claro que existiría un consumo estable en el tiempo, sin grandes variaciones en la media a largo plazo.…”