Publications by authors named "K Rouffiac"
Article Synopsis
- COVID-19 lockdown measures led to increased psychoactive substance use, with studies showing a rise in both alcohol and cannabis consumption during this period.
- Our research analyzed data from an international survey focusing on cannabis use changes between March and October 2020, revealing a drop in average consumption and a mix of users starting and stopping the drug.
- Findings indicated that cannabis users were generally less satisfied with government measures, more concerned about the economic impacts of COVID-19, and exhibited specific risk factors for use, including age, health concerns, and feelings of boredom.
Background: This paper is a follow-up study continuing the COVISTRESS network previous research regarding health-related determinants.
Objective: The aim was to identify the main consequences of COVID-19 lockdown on Body Mass Index and Perceived Fragility, related to Physical Activity (PA), for different categories of populations, worldwide.
Design: The study design included an online survey, during the first wave of COVID-19 lockdown, across different world regions.
Globally the COVID-19 pandemic outbreak has triggered an economic downturn and a rise in unemployment. As a result, global communities have had to face physical, health, psychological and socio-economical related stressors. The purpose of this study was to assess and report the impact of isolation and effect of coronavirus on selected psychological correlates associated with emotions.
View Article and Find Full Text PDFA lockdown of people has been used as an efficient public health measure to fight against the exponential spread of the coronavirus disease (Covid-19) and allows the health system to manage the number of patients. The aim of this study (clinicaltrials.gov NCT00430818) was to evaluate the impact of both perceived stress aroused by Covid-19 and of emotions triggered by the lockdown situation on the individual experience of time.
View Article and Find Full Text PDFA Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.
IEEE Trans Nanobioscience
October 2015
Clustering is a set of techniques of the statistical learning aimed at finding structures of heterogeneous partitions grouping homogenous data called clusters. There are several fields in which clustering was successfully applied, such as medicine, biology, finance, economics, etc. In this paper, we introduce the notion of clustering in multifactorial data analysis problems.
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