Functional health status, morbidity and mortality are determined partly by health
behaviours (World Health Organization, 2002), which have determinants of their
own. Personality traits, such as Conscientiousness, have a strong association
with health behaviours (Bogg & Roberts, 2004). There is a less consistent and
generally weaker association between traits and health outcomes (e.g. Neuroticism
and mortality). The central problem in this thesis is how to measure,
model, maximize, and extend trait-health associations. Conceptual issues associated
with modelling traits and health are discussed in chapter one. The next
three chapters concern such measurement issues about: personality traits (chapter
two), health behaviours (chapter three) and health outcomes, with particular
reference to functional health status (chapter four). These chapters are followed
by a move to modelling (chapter five), with particular reference to the
generalized latent variable modelling (LVM) framework (Muth´en & Muth´en,
1998–2007). The HAPPLE study is introduced (chapter six) which is used to
model associations between Conscientiousness and health criteria within the
LVMframework (chapter seven). Moving beyond self-reported outcomes, which
are a mono-method approach, the role of multiple health behaviours in predicting
cardiovascular mortality is considered (chapter eight). In a third section,
cortisol is introduced, which is a biomarker of stress reactivity. The diurnal profile
of cortisol output is described (chapter nine). Latent growth curve modelling
is used to illustrate its association with Neuroticism, in a sample of student volunteers
(chapter 10). Taken together, the results highlight the need for a general framework of modelling techniques, in personality-health research. I conclude
that biopsychosocial models with excellent explanatory power, which are still
parsimonious, can be achieved with LVM and its extensions. However, trait researchers
will need to state more clearly the intended destinations of their work
in order to attract contributions from, and share knowledge with, other disciplines