RESUMO
OBJECTIVE: To (a) investigate the prevalence of type-D personality (the conjoint effects of negative affectivity and social inhibition) in a healthy British and Irish population; (b) to test the influence of type-D on health-related behavior, and (c) to determine if these relationships are explained by neuroticism. METHODS: A cross-sectional design was employed; 1012 healthy young adults (225 males, 787 females, mean age 20.5 years) from the United Kingdom and Ireland completed measures of type-D personality, health behaviors, social support, and neuroticism. RESULTS: The prevalence of type-D was found to be 38.5%, significantly higher than that reported in other European countries. In addition, type-D individuals reported performing significantly fewer health-related behaviors and lower levels of social support than non-type-D individuals. These relationships remained significant after controlling for neuroticism. CONCLUSION: These findings provide new evidence on type-D and suggest a role for health-related behavior in explaining the link between type-D and poor clinical prognosis in cardiac patients.
Assuntos
Afeto , Comportamentos Relacionados com a Saúde , Inibição Psicológica , Desenvolvimento da Personalidade , Comportamento Social , Apoio Social , Adolescente , Adulto , Doença das Coronárias/psicologia , Estudos Transversais , Feminino , Inquéritos Epidemiológicos , Humanos , Masculino , Pessoa de Meia-Idade , Transtornos Neuróticos/diagnóstico , Transtornos Neuróticos/psicologia , Determinação da Personalidade , Fatores de Risco , Estudantes/psicologia , Reino UnidoRESUMO
Measures of icon designs rely heavily on surveys of the perceptions of population samples. Thus, measuring the extent to which changes in the structure of an icon will alter its perceived complexity can be costly and slow. An automated system capable of producing reliable estimates of perceived complexity could reduce development costs and time. Measures of icon complexity developed by Garcia, Badre, and Stasko (1994) and McDougall, Curry, and de Bruijn (1999) were correlated with six icon properties measured using Matlab (MathWorks, 2001) software, which uses image-processing techniques to measure icon properties. The six icon properties measured were icon foreground, the number of objects in an icon, the number of holes in those objects, and two calculations of icon edges and homogeneity in icon structure. The strongest correlates with human judgments of perceived icon complexity (McDougall et al., 1999) were structural variability (r(s) = .65) and edge information (r(s) = .64).