Pooled Time Series Modeling Reveals Smoking Habit Memory Pattern

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

Introduction: The COVID-19 pandemic that hit Spain during March 2020 forced the strict confinement of the population for 2 months. The objectives of this study were (a) to assess the magnitude and duration of the influence of confinement on people’s Distress, (b) to study the temporal sequence of stress, and (c) to show how different day-to-day activities and personal variables influence perceived Distress levels. Method: A daily registration was completed by 123 people, with ages ranging from 21 to 75 years old (X = 43, SD = 10 years), of which there were 40 men (32%) and 83 females (68%). During 45 days of lockdown, from March 19th to May 3rd, participants were asked to respond to a socio-demographic survey and make daily records comprising the MASQ-D30 and some day-to-day behaviors. Pooled time series was applied to establish what effect time had on the dependent variable. Results: Distress has a 14-day autoregressive function and gender, physical activity, sexual activity, listening to music, and teleworking also influence Distress. It has been hypothesized that the intercept presents variability at level 2 (individual), but it has not been significant. Interactions between Gender—Telecommuting, and Gender—Physical Activity were observed. Approximately 66% of the variance of Distress was explained (R 2 = 0.663). Discussion: At the beginning of the lockdown, the average levels of Distress were well above the levels of the end (z = 3.301). The individuals in the sample have followed a very similar process in the development of Distress. During the lockdown, the “memory” of Distress was 2 weeks. Our results indicate that levels of Distress depend on activities during lockdown. Interactions exist between gender and some behavioral variables that barely influence Distress in men but decrease Distress in women. The importance of routine maintenance and gender differences must be considered to propose future interventions during confinement

Population consequences of the Deepwater Horizon oil spill on pelagic cetaceans

This research was made possible by a grant from the Gulf of Mexico Research Initiative to the Consortium for Advanced Research on Marine Mammal Health Assessment (CARMMHA). T.A.M. acknowledges partial support by CEAUL (funded by FCT−Fundação para a Ciência e a Tecnologia, Portugal, through project UIDB/00006/2020).The Deepwater Horizon disaster resulted in the release of 490000 m3 of oil into the northern Gulf of Mexico. We quantified population consequences for pelagic cetaceans, including sperm whales, beaked whales and 11 species of delphinids. We used existing spatial density models to establish pre-spill population size and distribution, and overlaid an oil footprint to estimate the proportion exposed to oil. This proportion ranged from 0.058 (Atlantic spotted dolphin, 95% CI = 0.041-0.078) to 0.377 (spinner dolphin, 95% CI = 0.217-0.555). We adapted a population dynamics model, developed for an estuarine population of bottlenose dolphins, to each pelagic species by scaling demographic parameters using literature-derived estimates of gestation duration. We used expert elicitation to translate knowledge from dedicated studies of oil effects on bottlenose dolphins to pelagic species and address how density dependence may affect reproduction. We quantified impact by comparing population trajectories under baseline and oil-impacted scenarios. The number of lost cetacean years (difference between trajectories, summed over years) ranged from 964 (short-finned pilot whale, 95% CI = 385-2291) to 32584 (oceanic bottlenose dolphin, 95% = CI 13377-71967). Maximum proportional population decrease ranged from 1.3% (Atlantic spotted dolphin 95% CI = 0.5-2.3) to 8.4% (spinner dolphin 95% CI = 3.2-17.7). Estimated time to recover to 95% of baseline was >10 yr for spinner dolphin (12 yr, 95% CI = 0-21) and sperm whale (11 yr, 95% CI = 0-21), while 7 taxonomic units remained within 95% of the baseline population size (time to recover, therefore, as per its definition, was 0). We investigated the sensitivity of results to alternative plausible inputs. Our methods are widely applicable for estimating population effects of stressors in the absence of direct measurements.Publisher PDFPeer reviewe

Survival, density, and abundance of common bottlenose dolphins in Barataria Bay (USA) following the Deepwater Horizon oil spill

To assess potential impacts of the Deepwater Horizon oil spill in April 2010, we conducted boat-based photo-identification surveys for common bottlenose dolphins Tursiops truncatus in Barataria Bay, Louisiana, USA (~230 km2, located 167 km WNW of the spill center). Crews logged 838 h of survey effort along pre-defined routes on 10 occasions between late June 2010 and early May 2014. We applied a previously unpublished spatial version of the robust design capture-recapture model to estimate survival and density. This model used photo locations to estimate density in the absence of study area boundaries and to separate mortality from permanent emigration. To estimate abundance, we applied density estimates to saltwater (salinity > ~8 ppt) areas of the bay where telemetry data suggested that dolphins reside. Annual dolphin survival varied between 0.80 and 0.85 (95% CIs varied from 0.77 to 0.90) over 3 yr following the Deepwater Horizon spill. In 2 non-oiled bays (in Florida and North Carolina), historic survival averages approximately 0.95. From June to November 2010, abundance increased from 1300 (95% CI ± ~130) to 3100 (95% CI ± ~400), then declined and remained between ~1600 and ~2400 individuals until spring 2013. In fall 2013 and spring 2014, abundance increased again to approximately 3100 individuals. Dolphin abundance prior to the spill was unknown, but we hypothesize that some dolphins moved out of the sampled area, probably northward into marshes, prior to initiation of our surveys in late June 2010, and later immigrated back into the sampled area.Publisher PDFPeer reviewe

G*Power: importancia del tamaño muestral en análisis de series temporales

Introducción: Los análisis de series temporales son especialmente útiles, sobre todo en disciplinas que requieren un seguimiento longitudinal minucioso. A pesar de su gran utilidad, son poco frecuentes en el campo de la psicología. Por este motivo, los autores de este trabajo utilizaron las series temporales para llevar a cabo un estudio sobre la adicción al tabaco. Los resultados mostraron que la conducta tabaquista seguía un modelo AR (2)(7)8, es decir, la muestra tenía una memoria de 56 días. El objetivo del presente trabajo es comprobar la potencia estadística y el tamaño del efecto del modelo que encontraron. Método: Dada la ausencia de información en la bibliografía previa, se realizó a posteriori un análisis de series temporales imitando los modelos encontrados en los estudios previos. Con la información obtenida, se calculó mediante el software G*Power si el tamaño muestral era suficientemente grande para tener un modelo con una buena potencia y un tamaño del efecto. Resultados: El output indica que se necesita un mínimo de 17 sujetos con 63 datos diarios cada uno (1071 datos en total) para tener un modelo con buena potencia estadística y un tamaño del efecto digno. Conclusión: Los análisis de series temporales tienen poca potencia, por lo que se necesitan registros con un número elevado de datos por sujeto. Además, la cantidad de sujetos para obtener una potencia y un tamaño del efecto adecuados debe ser verificado mediante estudios previos o, si no es posible, mediante análisis a posteriori.Introduction: Time series analysis is particularly useful, especially in disciplines that require close longitudinal monitoring. Despite their great usefulness, its use is not common in fields such as psychology. For this reason, the authors of this work used time series to carry out a study on tobacco addiction. The results showed that tobacco behaviour followed an AR (2)(7) 8 model, that is, the sample had a 56-day memory. The objective of the present work is to verify the statistical power and the effect size of the model that they found. Method: Given the absence of information in the previous references, an analysis of time series was performed a posteriori imitating the models founded in the previous studies. It was calculated using G*Power software if our sample size is large enough to obtain a model with statistical power and a good effect size. Results: The output indicates that a minimum of 17 subjects are needed, with 63 data each day (a total of 1071 data) to obtain a model with a good statistical power and effect size. Conclusion: To sum up, we conclude with the affirmation that time series analysis has a poor statistical power, so samples for this type of analysis should be quite large. Furthermore, the ideal number of subjects to obtain an adequate statistical power and an effect size should be checked by a previous study or, if that is not possible, a posteriori analysis