Emotional Impact of Graphic Health Warnings on Tobacco Packaging: Analysis of Their Content.

The use of graphic health warnings (GHWs) on tobacco packaging is one of the most widely used public health strategies worldwide, but there is little evidence of the emotional impact of its content and craving they generate. The objective of the present study was to evaluate the emotional and craving responses to GHW content. The study included 300 men and women of different ages (adolescents, young adults, and adults), both smokers and non-smokers. We evaluated the participants' emotional and craving responses to 16 GHWs with different content (i.e., cancer, cardiovascular disease, abortion, and childhood illness). We analyzed the effects of sex, smoking status, and age. GHWs exhibited the capacity to elicit aversive emotional states, with low levels of arousal and high levels of dominance and did not produce craving. GHWs that showed images of cancer and abortion had the greatest emotional impact. Non-smoking adolescent females experienced the greatest emotional impact of GHWs. The results suggest that GHWs effectively reduce the attractiveness of cigarette packages and discourage consumption, and increasing the size of GHWs may favor avoidance of the package. GHWs that depicted explicit threats had a greater emotional impact, especially in women.

Stop! Graphic Health Warnings Enhance Inhibitory Control in Adolescents: An Event-Related Potential Study.

INTRODUCTION: The majority of smokers begin consumption in adolescence and the earlier initiation of cigarette smoking is associated with a greater likelihood of cigarette dependence. Graphic health warnings (GHW) are one of the most used strategies to communicate the consequences of cigarette smoking, but little is known about their ability to increase inhibitory control and thus prevent consumption. The objective of the present study was to evaluate the effects of different sizes of GHWs on inhibitory control in adolescents. We hypothesized that GHWs promote inhibitory control, and increasing GHW size, enhance inhibitory control. METHODS: Fifty-nine participants completed a Go/No-Go task during electroencephalographic recording. The No-Go stimuli were pictures of cigarette packs without GHWs, and cigarette packs with GHWs that covered 30% or 60% of the front (main side) of the pack. The event-related potential N200 component and behavioral measures in the Go/No-Go task were analyzed. RESULTS: Separate mixed-model analysis of variance (ANOVAs) were used for N200 component (amplitude and latency) and behavioral data. The GHWs increased the amplitude of the N200 potential, especially GHWs that covered 60% of the front of the pack. The behavioral data showed that GHWs that covered 60% of the front of the pack generated higher a percentage of accuracy in No-Go trials (ie, fewer commission errors). CONCLUSIONS: These results suggest that GHWs increase inhibitory control in adolescents, especially when the GHWs cover 60% of the front of the cigarette pack. IMPLICATIONS: GHWs with an increased size (60% of the front of the cigarette pack vs. 30%, the minimum size, proposed by the World Health Organization) recruit additional cognitive resources and thus can effectively increase inhibitory control both in adolescent smokers and nonsmokers. Accordingly, the use of larger GHW has the potential of becoming an effective public policy strategy to inhibit smoking in adolescents.

Size matters in the case of graphic health warnings: Evidence from physiological measures.

OBJECTIVES: >50 countries use graphic health warnings (GHWs) with the minimum size that is recommended by the World Health Organization (WHO). The aim of the present study was to evaluate the effect of the size of GHWs on physiological responses that serve as indices of arousal and aversive motivation in nonsmokers, weekly smokers, and daily smokers. METHODS: The skin conductance response, corrugator muscle activity, and startle reflex were recorded in 35 nonsmokers, 35 weekly smokers, and 35 daily smokers while they observed pictures of cigarette packs without GHWs, cigarette packs with GHWs that covered 30% of the pack, and cigarette packs with GHWs that covered 60% of the pack. RESULTS: Cigarette packs with 30% GHWs did not generate significantly higher responses on any of the physiological measures compared with cigarette packs without GHWs. Conversely, cigarette packs with GHWs that covered 60% of the pack generated a greater skin conductance response, greater corrugator muscle activity, and an increase in the startle reflex compared with cigarette packs without GHWs. No significant differences were found between groups in any of the physiological measures. CONCLUSIONS: The minimum size of GHW that is recommended by the WHO is insufficient to generate an emotional response that favors avoidance of the cigarette pack. GHW that cover 60% of the cigarette pack significantly reduced the attractiveness of the tobacco packaging and generated greater arousal responses.

Phonocardiogram classification using deep neural networks and weighted probability comparisons.

Cardiac auscultation is one of the most conventional approaches for the initial assessment of heart disease, however the technique is highly user-dependent and with low repeatability. Several computational approaches based on the analysis of the phonocardiograms (PCG) have been proposed to classify heart sounds into normal or abnormal, but most often do not achieve acceptable levels of sensitivity (Se) and specificity (Sp) or require the use of special hardware. We propose a novel approach for classification of PCG. First, the system makes use of deep neural networks for computing individual cardiac cycle probabilities, followed by classification using weighted probability comparisons. The system was tested on an extended dataset consisting of a balanced sample of 18179 normal and abnormal cycles, achieving Se and Sp values of 91.3% and 93.8% respectively. In addition, the system overcomes previous limitations since it was trained with a balanced sample; also, the decision factor used during the classification stage allows to control the trade-off between Se and Sp, making the proposed system suitable for clinical applications.

Anatomical Regurgitant Orifice Detection and Quantification from 3-D Echocardiographic Images.

The vena contracta and effective regurgitant orifice area (EROA) are currently used for the clinical assessment of mitral regurgitation (MR) from 2-D color Doppler imaging. In addition to being highly user dependent and having low repeatability, these methods do not represent accurately the anatomic regurgitant orifice (ARO), which affects the adequate assessment of MR patients. We propose a novel method for semi-automatic detection and quantitative assessment of the 3-D ARO shape from 3-D transesophageal echocardiographic images. The algorithm was tested on a set of 25 patients with MR, and compared with EROA for validation. Results indicate the robustness of the proposed approach, with low variability in relation to different settings of user-defined segmentation parameters. Although EROA and ARO exhibited a good correlation (r = 0.8), relatively large biases were measured, indicating that EROA probably underestimates the real shape and size of the regurgitant orifice. Along with the higher reproducibility of the proposed approach, this highlights the limitations of current clinical approaches and underlines the importance of accurate assessment of the ARO shape for diagnosis and treatment in MR patients.

Semi-automated segmentation and quantification of mitral annulus and leaflets from transesophageal 3-D echocardiographic images.

Quantification of three-dimensional (3-D) morphology of the mitral valve (MV) using real-time 3-D transesophageal echocardiography (RT3-D TEE) has proved to be a valuable tool for the assessment of MV pathologies, but of limited use in clinical practice because it relies on user-intensive approaches. This study presents a new algorithm for the segmentation and morphologic quantification of the mitral annulus (MA) and mitral leaflets (ML) in closed valve configuration from RT3-D TEE volumes. Following initialization, the MA and the ML and the coaptation line (CL) are automatically obtained in 3-D. Validation with manual tracings was performed on 33 patients, resulting in segmentation errors in the order of 0.7 mm and 0.6 mm for the MA and ML segmentation, in addition to good intra- and inter-observer reproducibility (coefficients of variation below 12% and 15%, respectively). The ability of the algorithm to assess different MV pathologies as well as repaired valves with implanted annular rings was also explored. The reported performance of the proposed fast, semi-automated MA and ML quantification makes it promising for future applications in clinical settings such as the operating room, where obtaining results in short time is important.

Semiautomated detection and quantification of aortic plaques from three-dimensional transesophageal echocardiography.

BACKGROUND: Aortic atherosclerosis is a risk factor for cerebrovascular events. Two-dimensional transesophageal echocardiographic quantification of descending aortic plaques is time-consuming and underestimates plaque burden. The aim of this study was to assess the feasibility and accuracy of a novel semiautomated program that uses three-dimensional (3D) transesophageal echocardiography to identify and quantify aortic plaque severity as determined by plaque thickness, volume, and number. The relationship between maximum plaque thickness and volume was also examined. METHODS: Descending aortic 3D transesophageal echocardiographic images from 58 patients were analyzed for plaque thickness, volume, and number using semiautomated custom software. The reference standard was manual assessment by an expert reader using 3D multiplanar reconstructions. Agreement and κ values were calculated to determine the program's accuracy against the reference standard. Correlation and bias were examined using linear regression and Bland-Altman statistics. Pearson's correlation was used to examine the relationship between maximum plaque thickness and volume. RESULTS: Analysis was possible in all patients. Overall agreement for the absolute presence or absence of plaque per patient was 95%. Agreement regarding the number of plaques per patient and plaque severity was high at 95% and 85%, respectively. Plaque volume was slightly underestimated by the program compared with manual measurements. The correlation between plaque thickness and volume was 0.56. CONCLUSIONS: The results of this study demonstrate that semiautomated plaque analysis of 3D transesophageal echocardiographic descending aortic data sets is feasible and accurate in determining plaque severity as measured by plaque thickness, volume, and number. This methodology allows the standardization of plaque quantification, which will improve its utility in clinical trials. A greater understanding of the importance of plaque thickness versus volume is needed.