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Three main objectives aimed at answering the following questions. Because of the limited sample size, it was not possible to create one overall statistical model including all variables at once to address the overall project design as illustrated by Figure 1 (see Introduction).

Instead, several models were built to address specific aspects and approach the overall outline as closely as possible. These models will be detailed in the methods section.

I. Does personality predict the occurrence of depression once demographic, psychosocial and health variables are accounted for?

This objective addressed the association of the five factors of personality with the presence of a mental illness, and more precisely a major depressive episode. Using a univariate approach, it first examined differences for the five factors in depressed patients compared to never-depressed controls, taking into account significant age group effects. Indeed, the literature review has revealed age-related changes in the five factors in the general population. Second, the personality-depression relationship was assessed in a multivariate approach, additionally taking into account demographic and psychosocial variables as well as physical comorbidities. More precisely, regression model A aimed to predict depression as defined by the severity of depressive symptoms. On the basis of the literature review, independent variables included individual psychological vulnerability markers such as the five personality factors, after accounting for demographic (age, sex, education), psychosocial (marital status, social support, level of dependency, stressful life events and their emotional impact) and physical health variables, which have been related to depressive mood in previous studies reported in literature. Besides the direct effect of age, its indirect effects on depression via its interactions with age sensitive variables (education, social support, stressful life events and physical illness) were also addressed.

Two aspects of this model were explored in further details. First, Neuroticism has been shown to cover state affect and trait variances, the variance of the first masking the variance of the second during acute episodes. Therefore, residuals of Neuroticism that are independent of mood state were introduced into model A to verify the hypothesis whether this trait variance of Neuroticism also significantly influences depression, or whether the Neuroticism-depression association only holds for the mood state variance of Neuroticism.

Second, some literature described that stressful life events predispose to depression, especially when they are interacting with specific personality dimensions. An alternative of

model A verified whether depression is best predicted by the isolated personality dimension Neuroticism, or by its interaction with life stress.

In this thesis manuscript, model A synthesized the most important findings. The article published in Aging & Mental Health (Appendix 6a) offered an introduction to the project highlighting the interest of including two different age groups when addressing the depression-personality relationship. The article submitted to Psychogeriatrics (Appendix 6b) put a specific focus on stressful life events and their relative importance for the depression-personality relationship, because of the predominant interest in stress-depression approaches in the existing literature.

II. Does personality moderate the influence of depression on quality of life outcome, once the influence of the symptom severity, demographic, psychosocial and health variables is accounted for?

In clinical practice and according to the WHO, good health is not merely the absence of symptoms, but also the presence of a high level of subjective quality of life. A second objective therefore concerned the assessment of patients’ level of quality of life consequently of their depressive state. The negative association between quality of life and depression has indeed been repeatedly established in previous studies. The quality of life model B also followed an integrative approach and included several additional explanatory variables. It assessed the prediction of quality of life by the five personality factors after having accounted for its association with the severity of depression, and for the influence of demographic (age, gender, education), psychosocial (marital status, social support, dependency, impact of significant life events) and physical burden variables. Interactions of those variables with depression were also included in model B to assess their indirect impact of quality of life by a moderating effect on depressive mood.

Model B was presented in the article submitted to the Journal of Geriatric Psychiatry and Neurology (Appendix 6c), stressing the interest of considering quality of life as additional outcome variable when addressing the depression-personality relationship.

III. Do predictors of depression and quality of life differ between young and old individuals?

While younger adults mostly present more severe forms of depression, older adults predominantly display milder yet more chronic forms of depression. This objective refers to the potential differences in predictor variables according to the patients’ life period. To

predict depression in old versus young age, model C took into account those variables that emerged as key variables in objective I, namely the severity of depression, impact of stressful life events, physical comorbidity and Neuroticism. Regarding quality of life, predictors first accounted for the nature of the depression, as defined by the severity of the current episode, disease duration and current suicide risk, before including comorbid physical disease and the five personality factors (model D). Further, model D analysed the moderating effect of the five personality dimensions on the depression-quality of life relationship by including the interactions between personality and depression to predict quality of life.

Model C focusing on the depression in old versus young age as presented in this thesis manuscript condenses the most important findings, and was discussed further in Article 6b.

Model D predicting quality of life in old respectively young participants is presented in Article 6c.

Hypotheses

I. On the basis of the literature review, which revealed differences in personality profiles in the presence of a major depressive episode, depression is estimated to show different associations with each of the five factors. In the simple univariate comparison between the four groups, depressed patients are expected to show higher mean levels of Neuroticism scores, and lower mean levels of Extraversion and Conscientiousness scores, compared to the never-depressed controls. No case-control differences are anticipated for Agreeableness and Openness to experience. The association between personality and depression is thought to hold despite and above the possible age main effects. Regarding age effects, on the basis of the literature review, older participants are expected to present lower mean level scores for Extraversion and Openness and higher scores for Agreeableness and Conscientiousness compared to younger adults. With respect to Neuroticism, given its strong interplay with depressive symptoms and the lower symptom intensity in old age, young age patients are expected to show even higher levels of Neuroticism than old age patients, reflecting an interaction between age and depression.

In the multivariate regression model predicting depression, older age, male gender, higher levels of education and of social support, as well as the presence of a partner are expected to play a protecting role. In contrast, physical illness, increased functional dependency, and the negative impact of stressful life events are thought to be detrimental factors for depressive illness. Regarding personality, while Neuroticism is expected to enhance

depression, Extraversion and Conscientiousness are expected to show a protective association.

II. Hypotheses of the present study will focus on the moderating effect of personality on the depression-quality of life relationship. Increased severity of depression is expected to be negatively related to quality of life, as has been previously established. Further, those variables showing the strongest impact on depression will also be included as predictors, because their interactions with depression possibly moderate the impact of depression on quality of life. Regarding covariates, female gender, living with a partner, higher age and higher levels of education and social support are anticipated to show a positive influence on quality of life, once the impact of depression has been accounted for. On the contrary, physical illness, increased dependency and life stress may show a negative association with quality of life after accounting for depression. Personality is expected to show a direct effect on quality of life, but also to interact with depression to moderate the effect of depressive symptoms on quality of life. Thus, higher levels of quality of life are thought to be directly or indirectly (via depression) associated with lower levels of Neuroticism, and higher levels of Extraversion, Openness, and Conscientiousness.

III. Regarding the distinctions between young and old age depression, explanatory variables are expected to have a different impact. On the basis of existing evidence and clinical observations, increased physical illness is most likely to be related to old age depression. In contrast, the relative importance of adverse effects of life events is expected to decrease in late life depression. Likewise, age-specific depression features, such as the severity of depressive symptoms, are thought to decrease the odds of depression in old age compared to in young age. Regarding personality factors, Neuroticism is expected to be a significant, yet less strong predictor for old depression in comparison to young age depression. This factor is positively correlated to the intensity of depression, but not reduced to a pure reflection of the depressive state as discussed in the literature.

With respect to the differences in predictor variables for quality of life in the two age groups, given the milder nature of depressive symptoms in old age, they are expected to show a weaker association with quality of life in old age, giving away the priority to background variables such as physical illness, Neuroticism, Extraversion and Conscientiousness. In young age, depressive symptoms and Neuroticism are expected to be the independent variables most strongly related to quality of life.

METHODS

The Faculty of Psychology and Educational Sciences of the Geneva University accepted the study design in April 2009. Initially the PhD thesis was supposed to be part of a larger research project including two grant applications for funding by the Swiss National Science Foundation (SNSF). I actively participated in the development and writing of these projects as a co-applicant.

The first SNSF project was developed by the Psychiatry and Mental Health Department of the Geneva University Hospital and focused on the differential impact of personality traits on depression outcome assessed by a two-year longitudinal design (SNSF 320030_138192, Canuto, Malafosse, Weber, Giannakopoulos & Hermann). Unfortunately, this funding was rejected three times between 2009 and 2011 and remains unsupported until today. Parts of the results of the present PhD thesis had been used as preliminary data to reinforce the last application in March 2011, unfortunately without changing its unsuccessful destiny.

Nevertheless, the contextual framework of the project was published in a position paper in November 2011 (Weber, Giannakopoulos & Canuto, 2011). The second SNSF project was part of a larger study on old age in Switzerland developed by the Centre for Interdisciplinary Gerontology (CIG), including a cross-sectional subpart on the role of personality and adaptive mechanisms for the regulation of well-being (Canuto, Perrig-Chiello & Spini). This second funding was first submitted and rejected in 2009, before being accepted in 2010 (CRSII1_129922 / 1 Sinergia).

Obviously, both collaborations influenced, at least partially, the methodological choices of the present project. However, since no funding was available neither at the beginning of the PhD thesis in April 2009, nor during the entire period of data collection, the thesis was finally conducted largely as an independent project and without financial support. The main practical consequence of this lack of funding is the absence of a longitudinal extension of the thesis. Fortunately, the absence of funding has not influence the cross-sectional data collection, which was achieved according to plan.

Study design

The PhD research design included a cross-sectional comparison amongst four groups, each composed of 40 participants. Group sizes were achieved as are detailed in Table 1. Two groups of patients with major depression composed respectively by young and old adults

were compared to two groups of never-depressed controls also divided into two age-matched groups.

Table 1: research plan (2 x 2 cross-sectional comparison)

Young (25-50 years) Old (60-85 years)

Never-depressed controls 51 51

Depressed patients 38 41

Originally a difference of 30 years between both age groups was determined to limit cohort effects and to allow for a larger age range between groups than within groups. The initial age range for the young age groups (30-45 years) had been defined to assess the five personality factors in adults who have achieved the maturation of their personality, and who are not yet experiencing the effects of aging, according to evidence from longitudinal lifetime studies (Roberts et al., 2008). To reduce the heterogeneity of the old age groups, their age range (60-75 years) was limited to the life period of the third age (youngest-old), which is classically distinguished from the fourth age (oldest-old), especially with regard to increasing frailty and comorbid physical and cognitive diseases after age 80 (Baltes & Smith, 2003).

However, recruitment difficulties revealed the need to adapt the design, and the age ranges were enlarged to 25-50 years respectively 60-85 years. This option allowed for achieving the planned sample size within the fixed timeline. Analysis of variance will follow the initial age (young/old) x depression (case/control) design. However, contrary to the initial plan, in the regression models, age will be analysed as a continuous variable, because the young/old dichotomy has became artificial, given the very large age ranges within each subgroup and the small 10-year gap between them (50-60 years).

Age groups

The final sample included 181 participants. With respect to the age subgroups, there were no significant mean age differences between young patients and controls respectively old patients and controls as illustrated by Figure 4.

Figure 4: age distribution in the four study groups

Patients Controls

Y O U N G

Mean= 37.37, SD= 7.31 (n=38) Mean= 35.31, SD= 5.38 (n=51)

O L D

Mean= 70.95, SD= 5.50 (n=41) Mean= 69.88, SD= 5.48 (n=51)

Participant selection

Selection criteria further included good French-speaking capacities, sufficient visual and hearing capacities to allow for self-assessment, and community-dwelling to guarantee for similar living situations between patients and controls. Inclusion in the depressed group required primary DSM-IV criteria for nonpsychotic major depression, and current combined pharmacotherapy and psychotherapy treatment as usual. Absence of a reported or recorded evidence of a lifetime or current psychiatric diagnosis was required for the control group, as revealed by participants’ medical record files when available, and as established by a standardized diagnostic assessment conducted by the study psychologists at study inclusion. Indeed, all participants were administered the structured Mini International Neuropsychiatric Interview (MINI-FR, Sheehan et al., 1998). The MINI is a structured

interview, which assesses the most frequent psychiatric disorders according to DSM-IV criteria. To assess history of psychiatric illnesses, information was extracted from medical records and confirmed by the referring psychiatrist.

Exclusion criteria for all participants comprised:

1. Neurological illnesses such as dementia or Parkinson

2. Primary diagnosis of dysthymic disorder, mania, hypomania or major depression with psychotic features

3. Psychotic disorders such as schizophrenia, schizoaffective disorder or delusional disorder

If either the MINI or the medical records revealed any of the three criteria, participants were excluded after the initial screening.

Recruitment procedure

The institutional board of the University Hospitals in Geneva approved the study in May 2009 (09-063/ Psy 09-007). Written informed consent was obtained from each participant prior to the study inclusion (Appendix 2). Participation was voluntary and unpaid. Control subjects were recruited through advertisements in local newspapers. Board-certified psychiatrists granted referral for patients from the outpatient settings of the Mental health and Psychiatry Department of the University Hospitals of Geneva (Dr J-P. Bachetta, Secteur Pâquis, for young age, and Dr C. Meiler, Centre Ambulatoire de Psychiatrie et Psychothérapie de l’Agé, CAPPA, for old age).

Recruitment started in June 2009. After 6 months of recruitment, 67 controls but only 12 (4 young and 8 old age) patients had been included. After consultation with the referring psychiatrists, age was identified as the most difficult selection criteria. Therefore, this selection criteria of the study was enlarged in January 2010 as follows: young age = 25-50 years and old age = 60-85 years. Most importantly, two more adult outpatients’ centres were contacted and agreed to participate in the study (Dr J. Bartholomei, Secteur 2 Jonction, and Dr A Meiler, Secteur 3 Servette). Additional approval was obtained from the ethical board for these extensions (February respectively June 2010).

After a total of 18 months of recruitment, 91 outpatients had been referred for study inclusion. Among them, 10 refused to participate, and 2 were excluded after inclusion screening (one presented a diagnosis of schizoaffective disorder and the second presented

a major episode in remission). 114 controls responded to the recruitment advertisements.

Among these, 10 were excluded because of psychiatric DSM-IV diagnosis (8 presented evidence for a past depression, 1 for current alcohol abuse, and 1 did not fulfil age criteria) and 2 refused to continue after the first contact. The final population sample included 79 outpatients (38 young and 41 old) and 102 never-depressed controls (51 young and 51 old).

To reduce the interviewer bias, all assessments were performed by the same two clinical psychologists (myself and Aline Mouchian), both experienced in mental health and old age psychiatry and certified in the same training in structured assessment tools such as the CIDI in large samples of old age adults. (Even though no reliability measure exists for the present thesis, Aline Mouchin and myself achieved very satisfactory inter-rater reliability in previous projects for the use of the same assessment tools than those of the present study). I assessed 71% (n=146) of the 205 participants by myself, and Aline Mouchian evaluated the remaining 29% (n=59). Interviews were conducted in the offices of the outpatients’ centres both for patients and controls, each interview lasting about 90 minutes. Data ware marked on successively numbered anonymous paper case record files and then entered into an excel sheet by one of the two psychologists and cross-checked by the second.

Assessment tools Depression features

To match clinical background in young and older outpatients, history of depression was extracted from medical records and confirmed by the referring psychiatrist: nature of depression (single/recurrent episode), past hospitalisations, duration of disease (years since onset), and duration of the present episode (months). The clinical study psychologist rated severity of depression on the Hamilton Rating Scale for Depression (HRSD, Hamilton, 1960), a 17-item questionnaire assessing low mood, insomnia, agitation, anxiety, and weight loss according to three- to five-point severity scale. The scores were added to obtain a total score (range 0-52) with higher scores reflecting higher symptom severity. The HRSD is generally accepted in young and in old age adults (Guelfi, 1993; Burns et al., 2004). To assess common co-morbid conditions such as anxiety and history of substance abuse, in addition to the categorical MINI assessment, severity of psychiatric symptoms was rated by the psychologist with the Health of the Nation Outcome Scale (HoNOS, Wing et al., 1998), composed of 12 items measuring behaviour, impairment, symptoms and social functioning, each rated on a 4-point severity ranking, higher scores indicating higher mental illness. The

French adult HoNOS has been validated in Lausanne (Lauzon et al. 2001) and the old age HoNOS 65+ has been validated in Geneva (Canuto et al., 2007).

Subjective quality of life

The World Health Organization Quality of Life - Bref (WHOQOL-Bref, WHOQOL Group, 1998) is a self-rated generic and multidimensional instrument comprising 2 separate items on overall quality of life and general health, plus 24 items assessed on a five-point agreement scale gathered into four domains: physical health, psychological health, social relationships and environment. The WHOQOL-Bref has been administered to younger adults French-speaking adults (Baumann et al., 2010), and also validated in older French-speaking samples (von Steinbüchel et al., 2006). Scores include four domain scores with range 4-20.

The total score was calculated as the sum of the four domain scores (range 16-80), higher

The total score was calculated as the sum of the four domain scores (range 16-80), higher