Acceptability of smokers of a conceptual cigarette tracker as wearable for smoking reduction.

OBJECTIVE: The study aims to explore smokers' acceptance of using a conceptual cigarette tracker like a cigarette filter for smoking cessation using the Technology Acceptance Model (TAM). Smokers presenting to the family medicine clinics at a tertiary care center were asked to complete an anonymous questionnaire. RESULTS: A total of 45 participants were included. Two-thirds of the smokers reported that they would like to try such a tracker and perceived its usefulness in reducing the number of daily cigarettes consumed and increasing the motivation to join a smoking cessation program. A range of 40-50% of the participants had a neutral attitude towards the visibility of the tracker and its effect on social acceptance and self-image. The structural equation model with latent variables path analysis showed that only perceived usefulness correlated to the intention to adopt with statistical significance. Visibility was correlated with intention to adopt with a marginal p-value of 0.061. Driven by perceived usefulness, smokers may buy or try a cigarette tracker for smoking reduction or cessation.

A comparison of artificial intelligence-based algorithms for the identification of patients with depressed right ventricular function from 2-dimentional echocardiography parameters and clinical features.

BACKGROUND: Recognizing low right ventricular (RV) function from 2-dimentiontial echocardiography (2D-ECHO) is challenging when parameters are contradictory. We aim to develop a model to predict low RV function integrating the various 2D-ECHO parameters in reference to cardiac magnetic resonance (CMR)-the gold standard. METHODS: We retrospectively identified patients who underwent a 2D-ECHO and a CMR within 3 months of each other at our institution (American University of Beirut Medical Center). We extracted three parameters (TAPSE, S' and FACRV) that are classically used to assess RV function. We have assessed the ability of 2D-ECHO derived parameters and clinical features to predict RV function measured by the gold standard CMR. We compared outcomes from four machine learning algorithms, widely used in the biomedical community to solve classification problems. RESULTS: One hundred fifty-five patients were identified and included in our study. Average age was 43±17.1 years old and 52/156 (33.3%) were females. According to CMR, 21 patients were identified to have RV dysfunction, with an RVEF of 34.7%±6.4%, as opposed to 54.7%±6.7% in the normal RV population (P<0.0001). The Random Forest model was able to detect low RV function with an AUC =0.80, while general linear regression performed poorly in our population with an AUC of 0.62. CONCLUSIONS: In this study, we trained and validated an ML-based algorithm that could detect low RV function from clinical and 2D-ECHO parameters. The algorithm has two advantages: first, it performed better than general linear regression, and second, it integrated the various 2D-ECHO parameters.