Aging & Mental Health, 2015 Vol. 19, No. 2, 151 158, http://dx.doi.org/10.1080/13607863.2014.920295
Personality, psychosocial and health-related predictors of quality of life in old age Kerstin Webera*, Alessandra Canutoa, Panteleimon Giannakopoulosb,c, Aline Mouchiana, Corina Meiler-Mititelub, Andri Meilerd, Fran¸c ois R. Herrmanne, Christophe Delaloyeb,f, Paolo Ghislettaf,g, Thierry Lecerff,g and Anik de Ribaupierref a
Division of Liaison Psychiatry and Crisis Intervention, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland; bDivision of Geriatric Psychiatry, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland; cDivision of Old Age Psychiatry, Department of Psychiatry, University Hospitals of the Canton of Vaud, Prilly, Switzerland; dDivision of Adult Psychiatry, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland; eDepartment of Internal Medicine, Rehabilitation and Geriatrics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Th^ onex, Switzerland; fFaculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland; gDistance Learning University Switzerland, Sierre, Switzerland (Received 25 November 2013; accepted 22 April 2014) Objectives: Beyond its well-documented association with depressive symptoms across the lifespan, at an individual level, quality of life may be determined by multiple factors: psychosocial characteristics, current physical health and long-term personality traits. Method: Quality of life was assessed in two distinct community-based age groups (89 young adults aged 36.2 § 6.3 and 92 older adults aged 70.4 § 5.5 years), each group equally including adults with and without acute depressive symptoms. Regression models were applied to explore the association between quality of life assessed with the World Health Organization Quality of Life - Bref (WHOQOL-Bref) and depression severity, education, social support, physical illness, as well as personality dimensions as defined by the Five-Factor Model. Results: In young age, higher quality of life was uniquely associated with lower severity of depressive symptoms. In contrast, in old age, higher quality of life was related to both lower levels of depressive mood and of physical illness. In this age group, a positive association was also found between quality of life and higher levels of Openness to experience and Agreeableness personality dimensions. Conclusion: Our data indicated that, in contrast to young cohorts, where acute depression is the main determinant of poor quality of life, physical illness and personality dimensions represent additional independent predictors of this variable in old age. This observation points to the need for concomitant consideration of physical and psychological determinants of quality of life in old age. Keywords: depressive symptoms; well-being; agreeableness; openness; physical health; personality; psychosocial and health-related predictors of quality of life in old age
Introduction The World Health Organization defines quality of life as the individual’s perception of their position in life according to their culture and value system, which is affected in a complex way by the person’s physical and psychological health, social relationships and personal beliefs (WHOQOL Group, 1998). Besides symptom severity, quality of life measures were introduced in a number of studies as an additional outcome variable to assess the subjective experience of depressed patients in younger cohorts (Bockerman, Johansson, & Saarni, 2011; Goldberg & Harrow, 2005; Pyne et al., 1997). In these individuals, major depression is typically considered as a complex, multi-factorial disorder associating stressful life events, physiological stressors, low social support, as well as personality traits (Kendler, Gardner, & Prescott, 2002. In old age, depressive mood has also emerged as a main predictor of decreased quality of life (Chan, Chien, Thompson, Chiu, & Lam, 2006; Doraiswamy, Khan, Donahue, & Richard, 2002; Naumann & Byrne, 2004) and even minor levels of depression have been related to a significant
*Corresponding author. Email: [email protected] Ó 2014 Taylor & Francis
decrease of quality of life in old age (Chachamovich, Fleck, Laidlaw, & Power, 2008). Unlike younger patients, only few previous studies adopted an integrative approach and simultaneously explored individual correlates of quality of life and their potential interaction with acute depressive symptoms in elderly cohorts. Most of them revealed that quality of life is influenced not only by the severity of depressive symptoms, but also gender, education and severity of selfreported physical comorbidities. Lower levels of education increase consistently the risk for depression in old age (Chang-Quan, Zheng-Rong, Yong-Hong, Yi-Zhou, & Qing-Xiu, 2010). Higher levels of education were positively related to quality of life, yet the opposite has also been shown (Ryff, 2008; Waddell & Jacobs-Lawson, 2010). Medical comorbidities have been reported to influence quality of life ratings in geriatric depression (Naumann & Byrne, 2004; Small et al., 1996). Besides these demographic factors, social psychology data pointed to the importance of supportive network and personality traits as determinants of quality of life in
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non-clinical samples. In younger adults, social support has been found to protect persons from potentially adverse effects of stressful live events (buffering model) (Cohen & Wills, 1985). In old age, perceived quality, rather than quantity, of supportive relationships and social networks per se have been related to quality of life (Blazer & Hybels, 2005; Karel, 1997; Landau & Litwin, 2001). Moreover, personality traits have emerged as powerful predictors of quality of life both in young and also in older cohorts (Friedman & Kern, 2014; Landau & Litwin, 2001; Ryff, 2008; Steel, Schmidt, & Schultz, 2008). Traditionally, hedonic quality of life, focusing on satisfaction, happiness and positive affect is distinguished from eudemonic or psychological quality of life, assessing purpose and meaning in life (Keyes, Shmotkin, & Ryff, 2002). Hedonic quality of life increases with age, is positively correlated with Extraversion, and Conscientiousness and negatively with Neuroticism. Eudemonic quality of life has been shown to decrease in advanced aging and is positively related to Openness (Ryff, 2008). The scarce studies on depression and quality of life available in old age included non-clinical samples only (Chan et al., 2006; Landau & Litwin, 2001; Ryff, 2008; Waddell & JacobsLawson, 2010). Other studies assessed quality of life in depressed psychiatric outpatients, yet merely compared their results with published norms, without appropriate control groups (Doraiswamy et al., 2002; Naumann & Byrne, 2004). In this study, we adopted a multi-factorial perspective and hypothesized that quality of life is explained by multiple factors not only in younger adults, but also in older populations. Based on the integrative works of Ryff (2008) in old age and lifespan perspective of Friedman (2000) and Friedman and Kern (2014), four sets of factors could impact decisively on the individual perception of the quality of life: psychosocial characteristics, presence of depression, current health variables such as comorbid physical illnesses, and long-term individual characteristics such as the five personality dimensions of Costa and McCrae (1992). Using a cross-sectional design, this study measured subjective quality of life in two groups of young and older adults carefully assessed for their depressive symptoms taking into account a combination of the above-mentioned factors. We expected these variables to impact on quality of life in addition to depression. Given the higher prevalence of physical comorbidities in old age, we postulated this variable to be of particular relevance in the old-age group.
Methods Sample characteristics Community-dwelling cases with good French-speaking capacities were initially considered. Participants were recruited through advertisements in local newspapers and by board certified psychiatrists in the outpatient settings of the Mental Health and Psychiatry Department of the University Hospitals of Geneva, Switzerland. Two distinct age groups were defined: young (aged from 25 to 50 years)
and older (aged from 60 to 85 years) adults. The young age range was defined to assess personality dimensions in adults who have achieved maturation of their personality, and who are not yet experiencing the effects of aging. This age limit was defined according to previous longitudinal lifetime studies on personality evolution (Roberts, Wood, & Caspi, 2008). To reduce the heterogeneity of the old-age group, the range was limited to the life period of the third age (youngest old), which is classically distinguished from the fourth age (oldest old) group, that more frequently displays frailty and comorbid physical and cognitive difficulties (Baltes & Smith, 2003). To create a sample representative of the whole spectrum of major depression, we included patients independently of their age at onset, as well as independently of the single/recurrent nature of their episode. The institutional ethical board approved the study, and after description of study aim and procedure, written informed consent was obtained from each participant prior to study inclusion. Participation was voluntary and unpaid. All participants were initially interviewed with the Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1998) by a senior psychiatrist or a trained clinical psychologist. Participants with neurological illness such as dementia or stroke, a diagnosis of psychotic disorders such as schizophrenia, schizoaffective disorder or delusional disorder and a diagnosis of mania, hypomania or major depression with psychotic features, according to their medical files or the MINI, were excluded from this study. All depressed participants received combined pharmacotherapy and psychotherapy treatment at the moment of study inclusion. The history of depression was assessed using information that was extracted from medical records and confirmed by the referring psychiatrist. The flow-diagram (Figure 1) illustrates the enrollment of the 181 participants. The final sample included 89 young [36.2 (6.3) years] and 92 older adults [70.4 (5.5) years]. The ratio of men-to-women was about two-third in both age groups (Table 1). Young adults presented with significantly higher levels of education and social support (number of trustworthy persons), and lower levels of physical comorbidities (CIRS score) compared to older adults (Table 1). Regarding the 79 participants diagnosed with an episode of major depression by their referring psychiatrist (confirmed by the MINI), age groups were matched for nature of depression (single/recurrent), length of current episode and past hospitalizations. Most of the participants in both age groups suffered from recurrent depressive episodes (Table 1). Young and older patients did not significantly differ with respect to the length of their current episode of depression, or presence of past hospitalizations. Both groups differed for disease duration and severity of depressive symptoms. As expected, disease duration (years since disease onset) was longer in older adults compared to younger adults. Regarding current symptom severity, while half of the participants exhibited no depressive symptoms in both age groups (55% 56%), younger patients predominantly showed severe forms of
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Figure 1. Enrollment flow of the 181 participants.
depression (23%) as opposed to older patients (1%). These latter participants mainly displayed mild levels of depressive symptoms (25% versus 7% in the younger group). Dependent variable The World Health Organization Quality of Life - Bref (WHOQOL-Bref; WHOQOL Group, 1998) is a self-rated
generic and multi-dimensional instrument of 24 items scored on a 5-point agreement scale that assess four domains: physical health, psychological health, social relationships and environment. Scores were transformed into four domain scores with range 4 20. The total score was calculated as the sum of the four domain scores, higher scores indicating higher levels of quality of life (16 80). The WHOQOL-Bref has been administered to younger French-speaking adults (Baumann, Erpelding, Regat,
Table 1. Sample characteristics for both age groups: depression, psychosocial variables, physical health, and personality variables (N D 181). Young (n D 89)
Gender (women %) Recurrent episodea Past hospitalizationsa Depression severityb HDRS 0 7 (none) HDRS 8 17 (mild) HDRS 18 25 (moderate) HDRS 26C (severe) Disease duration (years)a Length current episode (months)a Years of education Social support (trustworthy persons) CIRS scorec Personality factor scores Neuroticism Extraversion Openness Agreeableness Conscientiousness a
Old (n D 92)
n
%
n
%
x2
56 33 42
63 87 16
69 33 63
75 80 26
3.09 0.58 3.59
50 6 12 21 Mean 9.2 6.1 15.9 5.7 1.3
56 7 14 23 SD 7.4 3.1 4.3 2.9 0.8
51 23 17 1 Mean 24.17 7.5 12.2 3.9 2.1
55 25 19 1 SD 19.9 2.9 4.1 2.9 0.7
28.97
< 0.001
F(1, 179) 12.77 4.39 34.11 17.87 50.17
p 0.001 0.039 < 0.001 < 0.001 < 0.001
97.0 107.9 121.3 126.6 118.5
33.0 23.4 21.9 17.6 23.0
87.4 97.8 108.2 134.2 115.3
27.2 19.7 18.6 15.1 22.1
4.64 9.77 18.56 9.70 0.90
0.032 0.002 < 0.001 0.002 0.343
Applies only to the 79 participants diagnosed with an episode of major depression (MINI). According to HDRS (Hamilton Depression Rating Scale) cut-offs. c CIRS D Cumulative Illness Rating Scale total score normalized with square-root transformation. b
p 0.079 0.447 0.058
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Collin, & Briancon, 2010) and also validated in older French-speaking samples (von Steinb€ uchel, Lischetzke, Gurny, & Eid, 2006). Independent variables Psychosocial characteristics In addition to gender, education level was assessed by the number of years of formal education. Subjective social support was self-estimated by participants as the number of trustworthy relationships (How many people can you rely upon in times of need?). Depression and physical health The severity of depression was measured by the Hamilton Rating Scale for Depression (HRSD; Guelfi, 1996; Hamilton, 1960), a 17item questionnaire assessing low mood, insomnia, agitation, anxiety and weight loss according to 3- to 5-point severity scale, rated by the study of psychiatrist or psychologist. The scores were added to obtain a total score and higher scores reflect higher symptom severity (0 52). The continuous HRSD score was used for further statistical analysis since this variable offers a dimensional assessment of the depressive symptoms, including the full range of symptom severity (absence to severe). The HRSD is widely accepted for such use both in young and older adults (Burns, Lawlor, & Craig, 2004; Guelfi, 1996). Physical illnesses were self-reported on the Cumulative Illness Rating Scale (CIRS). A cumulative score is derived from ratings of severity of impairment in each of the 13 organ systems. The CIRS has been successfully used in both younger adults and older adults (de Decker, 2009; Linn, Linn, & Gurel, 1969). Personality traits The five personality factors were self-assessed with the French Neuroticism-ExtraversionOpenness (NEO) Personality Inventory-Revised version (NEO PI-R; Rolland, 1998). Internationally recognized as a gold standard, it consists of 240 statements, self-rated on a five-point agreement scale grouped into five main domains defined as follows: Neuroticism contrasts emotional stability with negative emotionality, such as feeling anxious, nervous, sad and tense. Extraversion implies an energetic approach toward the social and material worlds and includes traits such as sociability, activity, assertiveness and positive emotionality. Openness to experience describes the breadth, depth, originality and complexity of an individual’s mental and experiential life. Agreeableness includes traits such as altruism, tender-mindedness, trust and modesty. Finally, Conscientiousness describes socially prescribed impulse control that facilitates taskand goal-directed behaviors, such as thinking before acting, delaying gratification, following norms and rules, and prioritizing tasks (Costa & McCrae, 1992). Empirical data confirmed the structural continuity of the five factors across age groups (Roberts et al., 2008). Statistical analyses Preliminary statistics explored group differences between the young and the older age group. Pearson’s chi-square test was applied for categorical variables: gender, depression severity (none/mild/moderate/severe) and type
(single/recurrent) as well as past hospitalization (yes/no). Analyses of variance (ANOVA) were used with age group (young versus older) as independent variable and the following continuous variables, respectively, as dependent variables: years of education, social support, months of current depressive episode (as marker of chronicity or resistance), years since depression onset (as marker of early vulnerability), as well as scores of the questionnaires Hamilton Depression Rating Scale (HDRS), CIRS and NEO PI-R. The assumption of normality was evaluated with the Shapiro Francia test. Normality of data was best achieved with a square-root transformation for CIRS and years of disease duration. All other continuous variables were normally distributed. To assess the age-related differences in the relationship between quality of life (WHOQOL-Bref) and the various predictor variables, the same hierarchical linear regression model was estimated separately for each age group. In order to assess the relative contribution of acute psychiatric, non-psychological and psychological factors in predicting quality of life, three explanatory domains were considered: depression/psychosocial and health characteristics/personality dimensions. The predictors were grouped in blocks that were entered successively into the regression models. Block 1 included depression symptom severity as assessed by the continuous HRSD score. Block 2 integrated gender, level of education (number of years), social support (number of trustworthy persons) and physical illness (CIRS score). To explore the importance of individual psychological vulnerabilities, block 3 added the five personality factors, namely Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness. Within each age group, the blocks were compared on the amount of explained variance (R2 or coefficient of determination) using incremental F ratios. Overall, statistical differences were considered significant if the p value was < 0.05. Statistics were performed with Statistical Package for the Social Sciences (SPSS) version 19.0. Results The level of quality of life was identical in both age groups, as scored on the WHOQOL-Bref [mean total score for young 60.9 (11.1) and older adults 60.2 (10.7)]. The first regression model with WHOQOL-Bref total score as dependent variable was conducted in older adults only (n D 92, Table 2). Sixty-five percent of the variance was explained by depression severity (HDRS score). Incremental R2 changes revealed that the inclusion of education, social support and physical illness (block 2) explaining an additional 7% of quality of life variance (DR2 D 0.07, F(4, 86) D 5.5, p D 0.001). Personality factors (block 3) further added an extra 6% (DR2 D 0.06, F(5, 81) D 4.2, p D 0.002) to reach a final prediction of 78% of the quality of life variance. In this last block, depression severity showed the strongest negative effect on quality of life (B D 0.72, SE D 0.12, p < 0.001), followed by physical illness (B D 2.16, SE D 0.84, p D 0.012). Both high levels of Openness to experience (B D 0.09, SE D 0.04, p D 0.040) and Agreeableness
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Table 2. Quality of life determinants (WHOQOL-Bref total score) in old-age group (N D 92). Regression coefficients Blocks
Determinants
1
(Constant) HRSDa (Constant) HRSD Genderb Education CIRSc Social support (Constant) HRSD Gender Education CIRS Social support Neuroticism Extraversion Openness Agreeableness Conscientiousness
2
3
B
SE
70.64 ¡1.15 68.12 ¡0.96 2.72 0.06 ¡3.03 0.48 43.11 ¡0.72 2.67 ¡0.08 ¡2.16 0.46 ¡0.05 0.02 0.09 0.08 0.06
1.04 0.09 3.91 0.09 1.43 0.17 0.89 0.25 8.66 0.12 1.42 0.17 0.84 0.23 0.03 0.04 0.04 0.04 0.03
Change statistics p
R2
df
0.65
(1,90)
< 0.001
0.72
(4,86)
0.001
0.78
(5,81)
0.002
P
< 0.001 < 0.001 0.060 0.702 0.001 0.058 < 0.001 0.063 0.629 0.012 0.052 0.126 0.689 0.040 0.048 0.081
HRSD D Hamilton Rating Scale for Depression. 1 D Male, 2 D Female. CIRS D Cumulative Illness Rating Scale total score normalized with square-root transformation.
a
b c
(B D 0.08, SE D 0.04, p D 0.048) had a significant positive effect on quality of life. In the younger age group (n D 89, Table 3), 76% of the quality of life variance was already explained by
depression severity alone (B D 0.82, SE D 0.05, p < 0.001). In contrast to the older cases, the inclusion of physical illness, social support and education did not significantly improve the percentage of WHOQOL-Bref
Table 3. Quality of life determinants (WHOQOL-Bref total score) in young age group (N D 89). Regression coefficients Blocks
Determinants
1
(Constant) HRSDa (Constant) HRSD Genderb Education CIRSc Social support (Constant) HRSD Gender Education CIRS Social support Neuroticism Extraversion Openness Agreeableness Conscientiousness
2
3
B
SE
70.61 ¡0.82 64.90 ¡0.74 0.22 0.15 ¡0.11 0.37 65.40 ¡0.57 1.46 0.11 ¡0.20 0.43 ¡0.06 ¡0.03 0.05 ¡0.05 0.05
0.82 0.05 3.81 0.07 1.23 0.16 0.89 0.23 7.57 0.09 1.33 0.16 0.85 0.24 0.03 0.04 0.03 0.04 0.03
Change statistics p
b
df
P
0.76
(1, 87)
< 0.001
0.77
(4, 83)
0.384
0.81
(5, 78)
0.023
< 0.001 < 0.001 0.858 0.335 0.899 0.110 < 0.001 0.276 0.494 0.819 0.077 0.045 0.422 0.170 0.174 0.102
HRSD D Hamilton Rating Scale for Depression. 1 D Male, 2 D Female. c CIRS D Cumulative Illness Rating Scale total score normalized with square-root transformation. a
R
2
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scores variance explained by the model (DR2 D 0.01, F(4, 83) D 1.05, p D 0.384). None of the five personality factors emerged as a significant individual predictor of quality of life in this age group after controlling for all other variables.
Discussion Psychosocial characteristics Landau and Litwin (2001) performed path analysis, which revealed that the Zung Self-Rating Depression Scale score, self-rated physical health, social network supportiveness and education affected life satisfaction. In this study, neither gender, level of education, nor subjective social network supportiveness showed a significant association with quality of life independently of the age group. Our results are consistent with those of Pyne et al. (1997), who performed a cross-sectional comparison of well-being in patients with major depression and healthy controls aged about 50 years revealing that quality of life is a sensitive indicator for morbidity and disease severity independently of patients’ age, gender, education or family history of mental illness.
Depression This data revealed significant age-related differences in the determinants of quality of life between young and older individuals. At first glance and in agreement with previous observations in older (Chan et al., 2006; Doraiswamy et al., 2002; Naumann & Byrne, 2004) and younger adults (Steel et al., 2008), this study confirms the strong negative relationship between depression and quality of life across the adult lifespan. However, results of this study show that the relative contribution of the depression severity differs in young and old age. Thus, contrary to our hypothesis, in young age, presence of depression alone allowed for determining low quality of life. In contrast, and in agreement with our hypothesis, determinants of quality of life in old age were multiple, including physical illness and personality traits, as detailed below.
Physical health Most importantly, physical health and personality dimensions independently determined quality of life in the old-age group. Earlier cross-sectional studies in older samples have revealed that quality of life is influenced not only by the severity of depressive symptoms, but also by the severity of self-reported physical comorbidities in community-dwelling adults (Naumann & Byrne, 2004; Small et al., 1996; Waddell & JacobsLawson, 2010). Landau and Litwin (2001) claimed that the relationship between psychosocial variables and quality of life in later life is mediated by psychological resources and perceived health. Individuals reporting poor health may be less able to participate in
meaningful activities and more likely to be afflicted by mobility problems that decrease simultaneously their social interactions and quality of life (Borglin, Jakobsson, Edberg, & Hallberg, 2005). Confirming our initial hypothesis, this finding may explain why physical illness was of greater importance for quality of life in the old-age group, which presented with higher levels of comorbidities compared to the younger one. Our results confirm the need for a careful assessment of physical health status when addressing the relationship between quality of life and depression in old age. Personality traits Our data revealed that some personality dimensions are associated with quality of life in old age, after controlling for acute depression features, physical health and psychosocial variables. To date, only a few studies have simultaneously considered quality of life and personality traits in old age, by means of a multi-factorial approach that adjusts for gender, education, social support and physical health status. Results revealed a positive association between quality of life and Openness to experience, respectively, Agreeableness, after adjusting for these variables. The WHOQOL-Bref scale used in this study is closer to the concept of psychological (eudemonic) quality of life since it assesses individuals’ perceptions of their position in life in the context of their value systems and goals, expectations, standards and concerns (WHOQOL Group, 1998). Higher WHOQOL-Bref scores in this study were significantly associated with higher levels of the Openness and Agreeableness dimensions of personality and were related neither to Neuroticism, nor to Extraversion or Conscientiousness. Openness to experience is defined by one’s receptivity to one’s feelings, intellectual curiosity, arts, values or new experiences. McCrae and Costa (1991) have suggested that Openness may amplify both negative and positive emotional reactions. In our study, Openness has a protective impact in older cases being associated with higher levels of quality of life. Agreeableness has also shown positive links to higher quality of life (McCrae & Costa, 1991). In old age, personality continues its growth and maturation, associated with increased levels of Agreeableness, eudemonic quality of life and wisdom (Staudinger & Kunzman, 2005). Agreeableness reflects one’s way of managing interpersonal relationships; namely trust, straightforwardness, altruism, conflict management, modesty and tender mindedness (Costa & McCrae, 1992). Our study confirms that this personality dimension plays a key role in the subjective perception of the quality of life in old age. Interestingly, previous studies have described a positive relationship between quality of life and both Openness and Agreeableness in younger non-clinical samples (Ryff, 2008; Steel et al., 2008). Indeed, in our young age group, the relatively high level of depression of participants might have masked the association between personality dimensions and quality of life, especially for Neuroticism. As has
Aging & Mental Health been previously stated (Pyne et al., 1997), a severe depressed mood state may influence self-reported personality dimensions, not allowing for disentangling the influence of depression from that of Neuroticism on quality of life. In contrast, this study confirms that oldage models gain from focusing on more complex analyses including several covariates when assessing quality of life determinants. Ryff (2008) called for integrative, person-centered methods, which combine several determinants (e.g. personality traits, psychosocial characteristics, level of depression) to account for variation in particular outcomes such as quality of life. The author stressed that aging and quality of life come together in different ways for different people, depending on many other factors. Following this line, in conclusion, our results stress the necessity to include multiple psychosocial background variables when assessing quality of life determinants in older adults, in contrast to younger age, where depressive symptoms remain the main determinant of quality of life. Strengths of this study include careful matching for the recurrent nature and length of depressive episodes, presence of past hospitalizations and outpatient treatment setting. Both age groups showed no differences for these variables. Trained clinicians, using well-established investigator-based assessment procedures, excluded other psychiatric disorders. Several study limitations should, however, be taken into account when interpreting these results. First, in the absence of a longitudinal design, we cannot exclude that the association between depression and quality of life persists as a trait characteristic after the remission of acute depressive symptoms. Second, a single item rather than a more detailed questionnaire assessed social support. The use of a single item question has the advantage of simplicity mainly for older participants. However, more items would produce replies that are less prone to distortion, and this would enable to decrease the random error of the measure. We cannot thus formally exclude that the absence of significant results for this variable may be due to the choice of its assessment method. Finally, and most importantly, other variables, such as genetic predisposal, coping strategies, religion beliefs or cognition, known to play a predominant role in quality of life, have not been included in this study (Jones, Rapport, Hanks, Lichtenberg, & Telmet, 2003; Waddell & Jacobs-Lawson, 2010). Future longitudinal studies in large communitybased cohorts including the above-mentioned variables are warranted to identify the relative impact of genetic, environmental and psychological factors on quality of life in old age. References Baltes, P.B., & Smith, J. (2003). New frontiers in the future of aging: From successful aging of the young old to the dilemmas of the fourth age. Gerontology, 49(2), 123 135. doi:67946 [pii] Baumann, C., Erpelding, M.L., Regat, S., Collin, J.F., & Briancon, S. (2010). The WHOQOL-Bref questionnaire: French
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adult population norms for the physical health, psychological health and social relationship dimensions. Revue d’Epidemiologie et de Sante Publique, 58(1), 33 39. doi: S0398-7620(09)00522-7[pii]10.1016/j.respe.2009.10.009 Blazer, D.G., & Hybels, C.F. (2005). Origins of depression in later life. Psychological Medicine, 35(9), 1241 1252. doi: S0033291705004411 [pii] 10.1017/S0033291705004411 Bockerman, P., Johansson, E., & Saarni, S.I. (2011). Do established health-related quality-of-life measures adequately capture the impact of chronic conditions on subjective wellbeing? Health Policy, 100(1), 91 95. doi:S0168-8510(10) 00302-7 [pii] 10.1016/j.healthpol.2010.10.008 Borglin, G., Jakobsson, U., Edberg, A.K., & Hallberg, I.R. (2005). Self-reported health complaints and their prediction of overall and health-related quality of life among elderly people. International Journal of Nursing Studies, 42(2), 147 158. doi: S0020748904000999 [pii]10.1016/j.ijnurstu.2004.06.003 Burns, A., Lawlor, B., & Craig, S. (2004). Assessment scales in old age psychiatry. Hampshire: Thomson Publishing Services. Chachamovich, E., Fleck, M., Laidlaw, K., & Power, M. (2008). Impact of major depression and sub-syndromal symptoms on quality of life and attitudes toward aging in an international sample of older adults. Gerontologist, 48(5), 593 602. doi:48/5/593 [pii] Chan, S.W., Chien, W.T., Thompson, D.R., Chiu, H.F., & Lam, L. (2006). Quality of life measures for depressed and nondepressed Chinese older people. International Journal of Geriatric Psychiatry, 21(11), 1086 1092. doi:10.1002/ gps.1611 Chang-Quan, H., Zheng-Rong, W., Yong-Hong, L., Yi-Zhou, X., & Qing-Xiu, L. (2010). Education and risk for late life depression: A meta-analysis of published literature. International Journal of Psychiatry in Medicine, 40(1), 109 124. doi:10.2190/pm.40.1.i Cohen, S., & Wills, T.A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98, 310 357. doi:10.1037/0033-2909.98.2.310 Costa, P.T., Jr, & McCrae, R.R. (1992). NEO PI-R professional manual. Odessa, FL: Psychological Assessment Resources. de Decker, L. (2009). Le Cumulative Illness Rating Scale. Annales de G erontologie, 2(1), 9 11. doi:10.1684/age. 2009.0022 Doraiswamy, P.M., Khan, Z.M., Donahue, R.M., & Richard, N.E. (2002). The spectrum of quality-of-life impairments in recurrent geriatric depression. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 57(2), M134 137. doi:10.1093/gerona/57.2.M134 Friedman, H.S. (2000). Long-term relations of personality and health: Dynamisms, mechanisms, tropisms. Journal of Personality, 68(6), 1089 1107. doi:10.1111/1467-6494. 00127 Friedman, H.S. & Kern, M.L. (2014). Personality, well-being, and health. Annual Review of Psychology, 65, 719 742. doi:10.1146/annurev-psych-010213-115123 Goldberg, J.F., & Harrow, M. (2005). Subjective life satisfaction and objective functional outcome in bipolar and unipolar mood disorders: A longitudinal analysis. Journal of Affective Disorders, 89(1 3), 79 89. doi:S0165-0327(05)00245-4 [pii]10.1016/j.jad.2005.08.008 Guelfi, J.D. (1996). L’echelle de depression de Hamilton [Hamilton depression scale]. In J.D. Guelfi (Ed.), L’ evaluation clinique standardis ee en psychiatrie [Standardized clinical assessment in psychiatry] (pp. 187 196). Lavaur: Editions Medicales Pierre Fabre. Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery and Psychiatry, 23, 56 62. doi:10.1136/jnnp.23.1.56 Jones, T., Rapport, L., Hanks, R., Lichtenberg, P., & Telmet, K. (2003). Cognitive and psychosocial predictors of subjective well-being in urban older adults. The Clinical Neuropsychologist, 17(1), 3 18. doi:10.1076/clin.17.1.3.15626
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Karel, M.J. (1997). Aging and depression: Vulnerability and stress across adulthood. Clinical Psychology Review, 17(8), 847 879. doi:S0272-7358(97)00053-6 [pii] Kendler, K.S., Gardner, C.O., & Prescott, C.A. (2002). Toward a comprehensive developmental model for major depression in women. The American Journal of Psychiatry, 159(7), 1133 1145. doi:10.1176/appi.ajp.159.7.1133 Keyes, C.L., Shmotkin, D., & Ryff, C.D. (2002). Optimizing well-being: The empirical encounter of two traditions. Journal of Personality and Social Psychology, 82(6), 1007 1022. doi:10.1037/0022-3514.82.6.1007 Landau, R., & Litwin, H. (2001). Subjective well-being among the old old: The role of health, personality and social support. International Journal of Aging and Human Development, 52(4), 265 280. doi:10.2190/rumt-ycdx-x5hp-p2vh Linn, B.S., Linn, M.W., & Gurel, L. (1969). Physical resistance and longevity. Gerontologia Clinica, 11(6), 362 370. doi:10.1159/000245253 McCrae, R.R., & Costa, P.T. (1991). Adding liebe und arbeit: The full five-factor model and well-being. Personality and Social Psychology Bulletin, 17, 227 232. doi:10.1177/ 014616729101700217 Naumann, V.J., & Byrne, G.J. (2004). WHOQOL-Bref as a measure of quality of life in older patients with depression. International Psychogeriatrics, 16(2), 159 173. doi:10.1017/ s1041610204000109 Pyne, J.M., Patterson, T.L., Kaplan, R.M., Gillin, J.C., Koch, W. L., & Grant, I. (1997). Assessment of the quality of life of patients with major depression. Psychiatric Services, 48(2), 224 230. Roberts, B.W., Wood, D., & Caspi, A. (2008) The development of personality traits in adulthood. In O.P. John, R.W. Robins, & L. A. Pervin (Eds.), Handbook of personality: Theory and practice (pp. 375 398). New York, NY: The Guilford Press. Rolland, J.P. (1998). Manuel de l’inventaire NEO PI-R (adaptation fran¸c aise) [Manual of the neo-pi-r, French adaptation]. Paris: ECPA.
Ryff, C.D. (2008). Challenges and opportunities at the interface of aging, personality and well-being. In O.P. Oliver, R.W. Robins, & L.A. Pervin (Eds.), Handbook of personality: Theory and research (3rd ed., pp. 399 418). New York, NY: Guilford Press. Sheehan, D.V., Lecrubier, Y., Sheehan, K.H., Amorim, P., Janavs, J., Weiller, E., . . . Dunbar, G.C. (1998). The miniinternational neuropsychiatric interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59(20), 22 33. doi:10.1016/s09249338(97)83296-8 Small, G.W., Birkett, M., Meyers, B.S., Koran, L.M., Bystritsky, A., & Nemeroff, C.B. (1996). Impact of physical illness on quality of life and antidepressant response in geriatric major depression. Fluoxetine collaborative study group. Journal of the American Geriatrics Society, 44(10), 1220 1225. Staudinger, U.M., & Kunzmann, U. (2005). Positive adult personality development: Adjustment and/or growth? European Psychologist, 10(4), 320 329. doi:10.27/10169040.10.4.320 Steel, P., Schmidt, J., & Shultz, J. (2008). Refining the relationship between personality and subjective well-being. Psychological Bulletin, 134(1), 138 161. doi:2007-19419-006 [pii] 10.1037/0033-2909.134.1.138 von Steinb€ uchel, N., Lischetzke, T., Gurny, M., & Eid, M. (2006). Assessing quality of life in older people: Psychometric properties of the WHOQOL-Bref. European Journal of Ageing, 3, 116 122. doi:10.1007/s10433-006-0024-2 Waddell, E.L., & Jacobs-Lawson, J.M. (2010). Predicting positive well-being in older men and women. International Journal of Aging and Human Development, 70(3), 181 197. doi:10.2190/ag.70.3.a WHOQOL Group. (1998). Development of the World Health Organization WHOQOL-Bref quality of life assessment. Psychological Medicine, 28, 551 558. doi:10.1017/ s0033291798006667
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Personality, psychosocial and health-related predictors of quality of life in old age Kerstin Webera*, Alessandra Canutoa, Panteleimon Giannakopoulosb,c, Aline Mouchiana, Corina Meiler-Mititelub, Andri Meilerd, Fran¸c ois R. Herrmanne, Christophe Delaloyeb,f, Paolo Ghislettaf,g, Thierry Lecerff,g and Anik de Ribaupierref a
Division of Liaison Psychiatry and Crisis Intervention, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland; bDivision of Geriatric Psychiatry, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland; cDivision of Old Age Psychiatry, Department of Psychiatry, University Hospitals of the Canton of Vaud, Prilly, Switzerland; dDivision of Adult Psychiatry, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland; eDepartment of Internal Medicine, Rehabilitation and Geriatrics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Th^ onex, Switzerland; fFaculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland; gDistance Learning University Switzerland, Sierre, Switzerland (Received 25 November 2013; accepted 22 April 2014) Objectives: Beyond its well-documented association with depressive symptoms across the lifespan, at an individual level, quality of life may be determined by multiple factors: psychosocial characteristics, current physical health and long-term personality traits. Method: Quality of life was assessed in two distinct community-based age groups (89 young adults aged 36.2 § 6.3 and 92 older adults aged 70.4 § 5.5 years), each group equally including adults with and without acute depressive symptoms. Regression models were applied to explore the association between quality of life assessed with the World Health Organization Quality of Life - Bref (WHOQOL-Bref) and depression severity, education, social support, physical illness, as well as personality dimensions as defined by the Five-Factor Model. Results: In young age, higher quality of life was uniquely associated with lower severity of depressive symptoms. In contrast, in old age, higher quality of life was related to both lower levels of depressive mood and of physical illness. In this age group, a positive association was also found between quality of life and higher levels of Openness to experience and Agreeableness personality dimensions. Conclusion: Our data indicated that, in contrast to young cohorts, where acute depression is the main determinant of poor quality of life, physical illness and personality dimensions represent additional independent predictors of this variable in old age. This observation points to the need for concomitant consideration of physical and psychological determinants of quality of life in old age. Keywords: depressive symptoms; well-being; agreeableness; openness; physical health; personality; psychosocial and health-related predictors of quality of life in old age
Introduction The World Health Organization defines quality of life as the individual’s perception of their position in life according to their culture and value system, which is affected in a complex way by the person’s physical and psychological health, social relationships and personal beliefs (WHOQOL Group, 1998). Besides symptom severity, quality of life measures were introduced in a number of studies as an additional outcome variable to assess the subjective experience of depressed patients in younger cohorts (Bockerman, Johansson, & Saarni, 2011; Goldberg & Harrow, 2005; Pyne et al., 1997). In these individuals, major depression is typically considered as a complex, multi-factorial disorder associating stressful life events, physiological stressors, low social support, as well as personality traits (Kendler, Gardner, & Prescott, 2002. In old age, depressive mood has also emerged as a main predictor of decreased quality of life (Chan, Chien, Thompson, Chiu, & Lam, 2006; Doraiswamy, Khan, Donahue, & Richard, 2002; Naumann & Byrne, 2004) and even minor levels of depression have been related to a significant
*Corresponding author. Email: [email protected] Ó 2014 Taylor & Francis
decrease of quality of life in old age (Chachamovich, Fleck, Laidlaw, & Power, 2008). Unlike younger patients, only few previous studies adopted an integrative approach and simultaneously explored individual correlates of quality of life and their potential interaction with acute depressive symptoms in elderly cohorts. Most of them revealed that quality of life is influenced not only by the severity of depressive symptoms, but also gender, education and severity of selfreported physical comorbidities. Lower levels of education increase consistently the risk for depression in old age (Chang-Quan, Zheng-Rong, Yong-Hong, Yi-Zhou, & Qing-Xiu, 2010). Higher levels of education were positively related to quality of life, yet the opposite has also been shown (Ryff, 2008; Waddell & Jacobs-Lawson, 2010). Medical comorbidities have been reported to influence quality of life ratings in geriatric depression (Naumann & Byrne, 2004; Small et al., 1996). Besides these demographic factors, social psychology data pointed to the importance of supportive network and personality traits as determinants of quality of life in
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non-clinical samples. In younger adults, social support has been found to protect persons from potentially adverse effects of stressful live events (buffering model) (Cohen & Wills, 1985). In old age, perceived quality, rather than quantity, of supportive relationships and social networks per se have been related to quality of life (Blazer & Hybels, 2005; Karel, 1997; Landau & Litwin, 2001). Moreover, personality traits have emerged as powerful predictors of quality of life both in young and also in older cohorts (Friedman & Kern, 2014; Landau & Litwin, 2001; Ryff, 2008; Steel, Schmidt, & Schultz, 2008). Traditionally, hedonic quality of life, focusing on satisfaction, happiness and positive affect is distinguished from eudemonic or psychological quality of life, assessing purpose and meaning in life (Keyes, Shmotkin, & Ryff, 2002). Hedonic quality of life increases with age, is positively correlated with Extraversion, and Conscientiousness and negatively with Neuroticism. Eudemonic quality of life has been shown to decrease in advanced aging and is positively related to Openness (Ryff, 2008). The scarce studies on depression and quality of life available in old age included non-clinical samples only (Chan et al., 2006; Landau & Litwin, 2001; Ryff, 2008; Waddell & JacobsLawson, 2010). Other studies assessed quality of life in depressed psychiatric outpatients, yet merely compared their results with published norms, without appropriate control groups (Doraiswamy et al., 2002; Naumann & Byrne, 2004). In this study, we adopted a multi-factorial perspective and hypothesized that quality of life is explained by multiple factors not only in younger adults, but also in older populations. Based on the integrative works of Ryff (2008) in old age and lifespan perspective of Friedman (2000) and Friedman and Kern (2014), four sets of factors could impact decisively on the individual perception of the quality of life: psychosocial characteristics, presence of depression, current health variables such as comorbid physical illnesses, and long-term individual characteristics such as the five personality dimensions of Costa and McCrae (1992). Using a cross-sectional design, this study measured subjective quality of life in two groups of young and older adults carefully assessed for their depressive symptoms taking into account a combination of the above-mentioned factors. We expected these variables to impact on quality of life in addition to depression. Given the higher prevalence of physical comorbidities in old age, we postulated this variable to be of particular relevance in the old-age group.
Methods Sample characteristics Community-dwelling cases with good French-speaking capacities were initially considered. Participants were recruited through advertisements in local newspapers and by board certified psychiatrists in the outpatient settings of the Mental Health and Psychiatry Department of the University Hospitals of Geneva, Switzerland. Two distinct age groups were defined: young (aged from 25 to 50 years)
and older (aged from 60 to 85 years) adults. The young age range was defined to assess personality dimensions in adults who have achieved maturation of their personality, and who are not yet experiencing the effects of aging. This age limit was defined according to previous longitudinal lifetime studies on personality evolution (Roberts, Wood, & Caspi, 2008). To reduce the heterogeneity of the old-age group, the range was limited to the life period of the third age (youngest old), which is classically distinguished from the fourth age (oldest old) group, that more frequently displays frailty and comorbid physical and cognitive difficulties (Baltes & Smith, 2003). To create a sample representative of the whole spectrum of major depression, we included patients independently of their age at onset, as well as independently of the single/recurrent nature of their episode. The institutional ethical board approved the study, and after description of study aim and procedure, written informed consent was obtained from each participant prior to study inclusion. Participation was voluntary and unpaid. All participants were initially interviewed with the Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1998) by a senior psychiatrist or a trained clinical psychologist. Participants with neurological illness such as dementia or stroke, a diagnosis of psychotic disorders such as schizophrenia, schizoaffective disorder or delusional disorder and a diagnosis of mania, hypomania or major depression with psychotic features, according to their medical files or the MINI, were excluded from this study. All depressed participants received combined pharmacotherapy and psychotherapy treatment at the moment of study inclusion. The history of depression was assessed using information that was extracted from medical records and confirmed by the referring psychiatrist. The flow-diagram (Figure 1) illustrates the enrollment of the 181 participants. The final sample included 89 young [36.2 (6.3) years] and 92 older adults [70.4 (5.5) years]. The ratio of men-to-women was about two-third in both age groups (Table 1). Young adults presented with significantly higher levels of education and social support (number of trustworthy persons), and lower levels of physical comorbidities (CIRS score) compared to older adults (Table 1). Regarding the 79 participants diagnosed with an episode of major depression by their referring psychiatrist (confirmed by the MINI), age groups were matched for nature of depression (single/recurrent), length of current episode and past hospitalizations. Most of the participants in both age groups suffered from recurrent depressive episodes (Table 1). Young and older patients did not significantly differ with respect to the length of their current episode of depression, or presence of past hospitalizations. Both groups differed for disease duration and severity of depressive symptoms. As expected, disease duration (years since disease onset) was longer in older adults compared to younger adults. Regarding current symptom severity, while half of the participants exhibited no depressive symptoms in both age groups (55% 56%), younger patients predominantly showed severe forms of
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Figure 1. Enrollment flow of the 181 participants.
depression (23%) as opposed to older patients (1%). These latter participants mainly displayed mild levels of depressive symptoms (25% versus 7% in the younger group). Dependent variable The World Health Organization Quality of Life - Bref (WHOQOL-Bref; WHOQOL Group, 1998) is a self-rated
generic and multi-dimensional instrument of 24 items scored on a 5-point agreement scale that assess four domains: physical health, psychological health, social relationships and environment. Scores were transformed into four domain scores with range 4 20. The total score was calculated as the sum of the four domain scores, higher scores indicating higher levels of quality of life (16 80). The WHOQOL-Bref has been administered to younger French-speaking adults (Baumann, Erpelding, Regat,
Table 1. Sample characteristics for both age groups: depression, psychosocial variables, physical health, and personality variables (N D 181). Young (n D 89)
Gender (women %) Recurrent episodea Past hospitalizationsa Depression severityb HDRS 0 7 (none) HDRS 8 17 (mild) HDRS 18 25 (moderate) HDRS 26C (severe) Disease duration (years)a Length current episode (months)a Years of education Social support (trustworthy persons) CIRS scorec Personality factor scores Neuroticism Extraversion Openness Agreeableness Conscientiousness a
Old (n D 92)
n
%
n
%
x2
56 33 42
63 87 16
69 33 63
75 80 26
3.09 0.58 3.59
50 6 12 21 Mean 9.2 6.1 15.9 5.7 1.3
56 7 14 23 SD 7.4 3.1 4.3 2.9 0.8
51 23 17 1 Mean 24.17 7.5 12.2 3.9 2.1
55 25 19 1 SD 19.9 2.9 4.1 2.9 0.7
28.97
< 0.001
F(1, 179) 12.77 4.39 34.11 17.87 50.17
p 0.001 0.039 < 0.001 < 0.001 < 0.001
97.0 107.9 121.3 126.6 118.5
33.0 23.4 21.9 17.6 23.0
87.4 97.8 108.2 134.2 115.3
27.2 19.7 18.6 15.1 22.1
4.64 9.77 18.56 9.70 0.90
0.032 0.002 < 0.001 0.002 0.343
Applies only to the 79 participants diagnosed with an episode of major depression (MINI). According to HDRS (Hamilton Depression Rating Scale) cut-offs. c CIRS D Cumulative Illness Rating Scale total score normalized with square-root transformation. b
p 0.079 0.447 0.058
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Collin, & Briancon, 2010) and also validated in older French-speaking samples (von Steinb€ uchel, Lischetzke, Gurny, & Eid, 2006). Independent variables Psychosocial characteristics In addition to gender, education level was assessed by the number of years of formal education. Subjective social support was self-estimated by participants as the number of trustworthy relationships (How many people can you rely upon in times of need?). Depression and physical health The severity of depression was measured by the Hamilton Rating Scale for Depression (HRSD; Guelfi, 1996; Hamilton, 1960), a 17item questionnaire assessing low mood, insomnia, agitation, anxiety and weight loss according to 3- to 5-point severity scale, rated by the study of psychiatrist or psychologist. The scores were added to obtain a total score and higher scores reflect higher symptom severity (0 52). The continuous HRSD score was used for further statistical analysis since this variable offers a dimensional assessment of the depressive symptoms, including the full range of symptom severity (absence to severe). The HRSD is widely accepted for such use both in young and older adults (Burns, Lawlor, & Craig, 2004; Guelfi, 1996). Physical illnesses were self-reported on the Cumulative Illness Rating Scale (CIRS). A cumulative score is derived from ratings of severity of impairment in each of the 13 organ systems. The CIRS has been successfully used in both younger adults and older adults (de Decker, 2009; Linn, Linn, & Gurel, 1969). Personality traits The five personality factors were self-assessed with the French Neuroticism-ExtraversionOpenness (NEO) Personality Inventory-Revised version (NEO PI-R; Rolland, 1998). Internationally recognized as a gold standard, it consists of 240 statements, self-rated on a five-point agreement scale grouped into five main domains defined as follows: Neuroticism contrasts emotional stability with negative emotionality, such as feeling anxious, nervous, sad and tense. Extraversion implies an energetic approach toward the social and material worlds and includes traits such as sociability, activity, assertiveness and positive emotionality. Openness to experience describes the breadth, depth, originality and complexity of an individual’s mental and experiential life. Agreeableness includes traits such as altruism, tender-mindedness, trust and modesty. Finally, Conscientiousness describes socially prescribed impulse control that facilitates taskand goal-directed behaviors, such as thinking before acting, delaying gratification, following norms and rules, and prioritizing tasks (Costa & McCrae, 1992). Empirical data confirmed the structural continuity of the five factors across age groups (Roberts et al., 2008). Statistical analyses Preliminary statistics explored group differences between the young and the older age group. Pearson’s chi-square test was applied for categorical variables: gender, depression severity (none/mild/moderate/severe) and type
(single/recurrent) as well as past hospitalization (yes/no). Analyses of variance (ANOVA) were used with age group (young versus older) as independent variable and the following continuous variables, respectively, as dependent variables: years of education, social support, months of current depressive episode (as marker of chronicity or resistance), years since depression onset (as marker of early vulnerability), as well as scores of the questionnaires Hamilton Depression Rating Scale (HDRS), CIRS and NEO PI-R. The assumption of normality was evaluated with the Shapiro Francia test. Normality of data was best achieved with a square-root transformation for CIRS and years of disease duration. All other continuous variables were normally distributed. To assess the age-related differences in the relationship between quality of life (WHOQOL-Bref) and the various predictor variables, the same hierarchical linear regression model was estimated separately for each age group. In order to assess the relative contribution of acute psychiatric, non-psychological and psychological factors in predicting quality of life, three explanatory domains were considered: depression/psychosocial and health characteristics/personality dimensions. The predictors were grouped in blocks that were entered successively into the regression models. Block 1 included depression symptom severity as assessed by the continuous HRSD score. Block 2 integrated gender, level of education (number of years), social support (number of trustworthy persons) and physical illness (CIRS score). To explore the importance of individual psychological vulnerabilities, block 3 added the five personality factors, namely Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness. Within each age group, the blocks were compared on the amount of explained variance (R2 or coefficient of determination) using incremental F ratios. Overall, statistical differences were considered significant if the p value was < 0.05. Statistics were performed with Statistical Package for the Social Sciences (SPSS) version 19.0. Results The level of quality of life was identical in both age groups, as scored on the WHOQOL-Bref [mean total score for young 60.9 (11.1) and older adults 60.2 (10.7)]. The first regression model with WHOQOL-Bref total score as dependent variable was conducted in older adults only (n D 92, Table 2). Sixty-five percent of the variance was explained by depression severity (HDRS score). Incremental R2 changes revealed that the inclusion of education, social support and physical illness (block 2) explaining an additional 7% of quality of life variance (DR2 D 0.07, F(4, 86) D 5.5, p D 0.001). Personality factors (block 3) further added an extra 6% (DR2 D 0.06, F(5, 81) D 4.2, p D 0.002) to reach a final prediction of 78% of the quality of life variance. In this last block, depression severity showed the strongest negative effect on quality of life (B D 0.72, SE D 0.12, p < 0.001), followed by physical illness (B D 2.16, SE D 0.84, p D 0.012). Both high levels of Openness to experience (B D 0.09, SE D 0.04, p D 0.040) and Agreeableness
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Table 2. Quality of life determinants (WHOQOL-Bref total score) in old-age group (N D 92). Regression coefficients Blocks
Determinants
1
(Constant) HRSDa (Constant) HRSD Genderb Education CIRSc Social support (Constant) HRSD Gender Education CIRS Social support Neuroticism Extraversion Openness Agreeableness Conscientiousness
2
3
B
SE
70.64 ¡1.15 68.12 ¡0.96 2.72 0.06 ¡3.03 0.48 43.11 ¡0.72 2.67 ¡0.08 ¡2.16 0.46 ¡0.05 0.02 0.09 0.08 0.06
1.04 0.09 3.91 0.09 1.43 0.17 0.89 0.25 8.66 0.12 1.42 0.17 0.84 0.23 0.03 0.04 0.04 0.04 0.03
Change statistics p
R2
df
0.65
(1,90)
< 0.001
0.72
(4,86)
0.001
0.78
(5,81)
0.002
P
< 0.001 < 0.001 0.060 0.702 0.001 0.058 < 0.001 0.063 0.629 0.012 0.052 0.126 0.689 0.040 0.048 0.081
HRSD D Hamilton Rating Scale for Depression. 1 D Male, 2 D Female. CIRS D Cumulative Illness Rating Scale total score normalized with square-root transformation.
a
b c
(B D 0.08, SE D 0.04, p D 0.048) had a significant positive effect on quality of life. In the younger age group (n D 89, Table 3), 76% of the quality of life variance was already explained by
depression severity alone (B D 0.82, SE D 0.05, p < 0.001). In contrast to the older cases, the inclusion of physical illness, social support and education did not significantly improve the percentage of WHOQOL-Bref
Table 3. Quality of life determinants (WHOQOL-Bref total score) in young age group (N D 89). Regression coefficients Blocks
Determinants
1
(Constant) HRSDa (Constant) HRSD Genderb Education CIRSc Social support (Constant) HRSD Gender Education CIRS Social support Neuroticism Extraversion Openness Agreeableness Conscientiousness
2
3
B
SE
70.61 ¡0.82 64.90 ¡0.74 0.22 0.15 ¡0.11 0.37 65.40 ¡0.57 1.46 0.11 ¡0.20 0.43 ¡0.06 ¡0.03 0.05 ¡0.05 0.05
0.82 0.05 3.81 0.07 1.23 0.16 0.89 0.23 7.57 0.09 1.33 0.16 0.85 0.24 0.03 0.04 0.03 0.04 0.03
Change statistics p
b
df
P
0.76
(1, 87)
< 0.001
0.77
(4, 83)
0.384
0.81
(5, 78)
0.023
< 0.001 < 0.001 0.858 0.335 0.899 0.110 < 0.001 0.276 0.494 0.819 0.077 0.045 0.422 0.170 0.174 0.102
HRSD D Hamilton Rating Scale for Depression. 1 D Male, 2 D Female. c CIRS D Cumulative Illness Rating Scale total score normalized with square-root transformation. a
R
2
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K. Weber et al.
scores variance explained by the model (DR2 D 0.01, F(4, 83) D 1.05, p D 0.384). None of the five personality factors emerged as a significant individual predictor of quality of life in this age group after controlling for all other variables.
Discussion Psychosocial characteristics Landau and Litwin (2001) performed path analysis, which revealed that the Zung Self-Rating Depression Scale score, self-rated physical health, social network supportiveness and education affected life satisfaction. In this study, neither gender, level of education, nor subjective social network supportiveness showed a significant association with quality of life independently of the age group. Our results are consistent with those of Pyne et al. (1997), who performed a cross-sectional comparison of well-being in patients with major depression and healthy controls aged about 50 years revealing that quality of life is a sensitive indicator for morbidity and disease severity independently of patients’ age, gender, education or family history of mental illness.
Depression This data revealed significant age-related differences in the determinants of quality of life between young and older individuals. At first glance and in agreement with previous observations in older (Chan et al., 2006; Doraiswamy et al., 2002; Naumann & Byrne, 2004) and younger adults (Steel et al., 2008), this study confirms the strong negative relationship between depression and quality of life across the adult lifespan. However, results of this study show that the relative contribution of the depression severity differs in young and old age. Thus, contrary to our hypothesis, in young age, presence of depression alone allowed for determining low quality of life. In contrast, and in agreement with our hypothesis, determinants of quality of life in old age were multiple, including physical illness and personality traits, as detailed below.
Physical health Most importantly, physical health and personality dimensions independently determined quality of life in the old-age group. Earlier cross-sectional studies in older samples have revealed that quality of life is influenced not only by the severity of depressive symptoms, but also by the severity of self-reported physical comorbidities in community-dwelling adults (Naumann & Byrne, 2004; Small et al., 1996; Waddell & JacobsLawson, 2010). Landau and Litwin (2001) claimed that the relationship between psychosocial variables and quality of life in later life is mediated by psychological resources and perceived health. Individuals reporting poor health may be less able to participate in
meaningful activities and more likely to be afflicted by mobility problems that decrease simultaneously their social interactions and quality of life (Borglin, Jakobsson, Edberg, & Hallberg, 2005). Confirming our initial hypothesis, this finding may explain why physical illness was of greater importance for quality of life in the old-age group, which presented with higher levels of comorbidities compared to the younger one. Our results confirm the need for a careful assessment of physical health status when addressing the relationship between quality of life and depression in old age. Personality traits Our data revealed that some personality dimensions are associated with quality of life in old age, after controlling for acute depression features, physical health and psychosocial variables. To date, only a few studies have simultaneously considered quality of life and personality traits in old age, by means of a multi-factorial approach that adjusts for gender, education, social support and physical health status. Results revealed a positive association between quality of life and Openness to experience, respectively, Agreeableness, after adjusting for these variables. The WHOQOL-Bref scale used in this study is closer to the concept of psychological (eudemonic) quality of life since it assesses individuals’ perceptions of their position in life in the context of their value systems and goals, expectations, standards and concerns (WHOQOL Group, 1998). Higher WHOQOL-Bref scores in this study were significantly associated with higher levels of the Openness and Agreeableness dimensions of personality and were related neither to Neuroticism, nor to Extraversion or Conscientiousness. Openness to experience is defined by one’s receptivity to one’s feelings, intellectual curiosity, arts, values or new experiences. McCrae and Costa (1991) have suggested that Openness may amplify both negative and positive emotional reactions. In our study, Openness has a protective impact in older cases being associated with higher levels of quality of life. Agreeableness has also shown positive links to higher quality of life (McCrae & Costa, 1991). In old age, personality continues its growth and maturation, associated with increased levels of Agreeableness, eudemonic quality of life and wisdom (Staudinger & Kunzman, 2005). Agreeableness reflects one’s way of managing interpersonal relationships; namely trust, straightforwardness, altruism, conflict management, modesty and tender mindedness (Costa & McCrae, 1992). Our study confirms that this personality dimension plays a key role in the subjective perception of the quality of life in old age. Interestingly, previous studies have described a positive relationship between quality of life and both Openness and Agreeableness in younger non-clinical samples (Ryff, 2008; Steel et al., 2008). Indeed, in our young age group, the relatively high level of depression of participants might have masked the association between personality dimensions and quality of life, especially for Neuroticism. As has
Aging & Mental Health been previously stated (Pyne et al., 1997), a severe depressed mood state may influence self-reported personality dimensions, not allowing for disentangling the influence of depression from that of Neuroticism on quality of life. In contrast, this study confirms that oldage models gain from focusing on more complex analyses including several covariates when assessing quality of life determinants. Ryff (2008) called for integrative, person-centered methods, which combine several determinants (e.g. personality traits, psychosocial characteristics, level of depression) to account for variation in particular outcomes such as quality of life. The author stressed that aging and quality of life come together in different ways for different people, depending on many other factors. Following this line, in conclusion, our results stress the necessity to include multiple psychosocial background variables when assessing quality of life determinants in older adults, in contrast to younger age, where depressive symptoms remain the main determinant of quality of life. Strengths of this study include careful matching for the recurrent nature and length of depressive episodes, presence of past hospitalizations and outpatient treatment setting. Both age groups showed no differences for these variables. Trained clinicians, using well-established investigator-based assessment procedures, excluded other psychiatric disorders. Several study limitations should, however, be taken into account when interpreting these results. First, in the absence of a longitudinal design, we cannot exclude that the association between depression and quality of life persists as a trait characteristic after the remission of acute depressive symptoms. Second, a single item rather than a more detailed questionnaire assessed social support. The use of a single item question has the advantage of simplicity mainly for older participants. However, more items would produce replies that are less prone to distortion, and this would enable to decrease the random error of the measure. We cannot thus formally exclude that the absence of significant results for this variable may be due to the choice of its assessment method. Finally, and most importantly, other variables, such as genetic predisposal, coping strategies, religion beliefs or cognition, known to play a predominant role in quality of life, have not been included in this study (Jones, Rapport, Hanks, Lichtenberg, & Telmet, 2003; Waddell & Jacobs-Lawson, 2010). Future longitudinal studies in large communitybased cohorts including the above-mentioned variables are warranted to identify the relative impact of genetic, environmental and psychological factors on quality of life in old age. References Baltes, P.B., & Smith, J. (2003). New frontiers in the future of aging: From successful aging of the young old to the dilemmas of the fourth age. Gerontology, 49(2), 123 135. doi:67946 [pii] Baumann, C., Erpelding, M.L., Regat, S., Collin, J.F., & Briancon, S. (2010). The WHOQOL-Bref questionnaire: French
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