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The effects of wisdom-related personality traits on caregivers’ health: an application of the resilience model

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Content 1

The Effects of Wisdom-related Personality Traits on Caregivers ’ Health:
An Application of the Resilience Model

Seungyoun Kim
Doctoral Dissertation
University of Southern California
Davis School of Gerontology
August 2015
2

Acknowledgements
This journey has been a challenge that taught me a lot about myself and the
people in my life. God, I am so grateful for the perseverance and strength you gave me
throughout this long journey. I am in appreciation of the grace that God has provided to
me by enlisting my wonderful husband and remarkable family and friends in my life.
This work is dedicated to my sons, Sunwoo & Philip, and my husband. To my husband,
the love of my life, I am so grateful to you. My heart is warmed by your enduring and genuine
support.
I gratefully acknowledge my advisor, Bob G. Knight, for his guidance, his
encouragement, and his resolute dedication to getting me through this program. I wish to thank
the members of my committee for their support and mentorship in the preparation of this
dissertation.

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List of Contents
Acknowledgements 2
List of Tables 4
List of Figures 6
Abstract 7
Introduction 9
Methods 30
Results 39
Discussion 45
References 63

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List of Tables
Table 1: Descriptive characteristics and high-risk cut-points of biomarkers

Table 2: Descriptive characteristics and distribution for all analytic variables by caregiving
status (The sample for mental and self-rated health analysis)

Table 3: Descriptive characteristics and distribution for all analytic variables by caregiving status
(The sample for biomarker analysis)

Table 4: Results of χ
2
tests and t-test comparing age and gender between caregivers and matched
non-caregivers

Table 5: The direct and indirect effects of wisdom-related personality traits on life satisfaction
among caregiving spouses

Table 6: The direct and indirect effects of wisdom-related personality traits on life satisfaction
among caregiving parents

Table 7: Correlation coefficients among key variables for caregiving adult-children in biomarker
sample

Table 8: The direct and indirect effects of wisdom-related personality traits on self-rated physical
health among non-caregivers matched to caregiving parents

Table 9: The direct and indirect effects of wisdom-related personality traits on self-rated physical
health among non-caregivers matched to caregiving adult-children

Table 10: Correlation coefficients among key variables for non-caregivers matched to
caregiving adult-children in biomarker sample

Table 11: The direct and indirect effect of wisdom-related personality traits on Hypothalamic
Pituitary Adrenal Axis among non-caregivers

Table 12: The direct and indirect effects of wisdom-related personality traits on Parasympathetic
Nervous System (PNS) among non-caregivers

Table 13: Correlation coefficients among key variables for ex-caregivers

Table 14: The direct and indirect effects of wisdom-related personality traits on life satisfaction
among ex-caregivers

Table 15: The direct and indirect effects of wisdom-related personality traits on self-rated
physical health among ex-caregivers

Table 16: Correlation coefficients among key variables for ex-caregivers in biomarker sample
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Table 17: The direct and indirect effect of wisdom-related personality traits on Metabolic-
glucose metabolism(MGM) among ex-caregivers

Table 18: The direct and indirect effect of wisdom-related personality traits on Hypothalamic
Pituitary Adrenal (HPA) Axis among ex-caregivers

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List of Figures
Figure 1: Possible mechanisms of psychosocial resources predicting caregivers ’ health

Figure 2: The conceptual model of the direct and indirect effects of wisdom-related personality
traits on caregivers ’ mental health

Figure 3: The conceptual model of the direct and indirect effects of wisdom-related personality
traits on caregivers ’ physical health (physiological indicators)

Figure 4: Illustration of direct and indirect effects of wisdom-related personality traits on health

Figure 5: The direct effect of a sense of mastery on Sympathetic Nervous System (SNS) among
non-caregivers

Figure 6: The direct effect of openness to experience on allostatic load among ex-caregivers

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Abstract
Models of resilience suggest that various psychosocial resources and their interactions facilitate
resilience while experiencing life challenges of caregiving. The wisdom-related personality traits
have been suggested as possible personal resources of resilience that predict positive health
outcomes of caregivers. By applying the model of resilience and the biopsychosocial approach,
this study examined the role of wisdom-related personality traits on c a re g iv e rs’ mental and
physical health across caregiving subgroups and ex-caregivers. The aims of this project were to
(1) examine the direct and indirect effects of wisdom-related personality traits through social
support on c a r e g iver s’ mental health (life satisfaction), (2) explore the association between
wisdom-related personality traits and caregivers ’ physical health measured according to a self-
rated scale and multiple biomarkers, (3) compare those effects among caregiving subgroups, and
(4) explore the role of wisdom-related personality traits and social support in ex-caregivers ’
mental and physical health. Using data from the survey of Midlife in the United States (MIDUS
II), the sample for mental health and self-rated physical health analysis consisted of caregiving
spouses (n=114), adult-children (n=275), parents (n=73), ex-caregivers (n=1056), and the
matched non-caregivers for each caregiving subgroup and ex-caregivers. For analyses of
physical health assessed by biomarker indices, caregivers (n=60) , the matched non-caregivers
(n=120), and ex-caregivers (n=120) were included. The simple mediation model (Preacher &
Hayes, 2008) revealed that openness to experience and spouse support were important
psychosocial resources related to better life satisfaction among caregiving spouses. I also found
that emotion regulation and social support from diverse relationships (spouse, family, and friends)
conferred the protective effects on life satisfaction among caregiving parents. There were no
differences in the effect of psychosocial resources on life satisfaction between caregiving adult-
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children and the matched caregivers. Compared to non-caregivers, the effects of wisdom-related
e r s ona i r a i s on c a r e g i ve r s physical health were not found in the current study. However,
the longer period caregiving (years of caregiving) led to the buffering effects of wisdom-related
e r s ona i r a i s on c a r e g i ve r s mental health. Finally, I found direct and indirect effects of
wisdom-related personality traits on life satisfaction and self-rated physical health among ex-
caregivers. Moreover, openness to experience was negatively related to allostatic load among ex-
caregivers. Overall, this study demonstrated that wisdom-related personality traits help
caregivers to improve their mental health and showed how the beneficial effects of those traits
differ by caregiving subgroups. Findings reveal potential areas for intervention and suggest
future directions for research, including the relationship between changes in wisdom-related
personality traits and c a r e g i v e r s health outcomes.

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The Effects of Wisdom-Related Personality Traits on Caregivers ’ Health:
An Application of the Resilience Model
The negative consequences of caregiving are evident and have dominated family
caregiving research (Yap, Luo, Ng, Chionh, Lim, & Goh, 2010). In recent years, however, a
growing number of researchers have become interested in finding positive psychological and
physical health outcomes among family caregivers (e.g., Tarlow, Wisniewski, Belle, Rubert, Ory,
& Gallagher-Thompson, 2004; Mausbach et al., 2011).
This research trend has been accelerated by increasing an emphasis on resilience studies
exploring positive health outcomes in reaction to acute and chronic stress situations. Resilience
refers to a positive adaptive process, that is, the maintenance or regaining of well-being in
conditions of stress (Rutten et al., 2013; Ryff, 2012). Models of resilience suggest that various
psychosocial resources and their interactions facilitate resilience while experiencing life
challenges of caregiving (e.g., Shirai, Koerner, & Kenyon, 2009). Positive health outcomes
during caregiving experiences highlight the importance of exploring psychosocial resources that
may protect caregivers from mental and physical risks.
Despite increasing attention to the positive relationship between psychosocial resources
and caregivers ’ health, the literature has some gaps in understanding the mechanisms by which
those resources may lead to positive health outcomes. First, previous studies did not consider
diverse psychosocial resources. Most studies have tended to focus on the role of the Big Five
personality traits (e.g., Koerner, Kenyon, & Shirai, 2009), sense of mastery (e.g., Roepke et al.,
2008), self-efficacy (e.g., Cheng et al., 2013), social support (e.g., Piercy et al., 2013), and
positive emotion (e.g., Monin, Schulz, Lemay, & Cook, 2012; Song, Mailick, Ryff, Coe,
Greenberg, & Hong, 2013). Some wisdom researchers suggest that wisdom-related personality
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traits help people to respond to life challenges, for example, caregiving, in a growth-conducive
way, and result in positive outcomes (Ardelt, 2005; Glück, 2011). Given that wisdom-related
personality traits are possible personal resources that predict positive health outcomes of
caregivers, understanding the association between the traits and caregivers ’ health is important.
Second, the current literature on resilience has shown that it is important to explore links
between psychological and biological aspects of resilience to understand its underlying
mechanisms (Rutten et al., 2013). Similarly, based on the biopsychosocial model, recent
caregiving studies have applied an integrative approach to explain the association between
caregiving stress and physical health (Kang & Marks, 2014; Roepke et al., 2011). Caregiving
research has broadened in focus from self-reported measures to more physiological indicators of
health (Harmell, Chattillion, Roepke, & Mausbach, 2011; Kim & Knight, 2008). Most previous
work, however, has used either a few individual biomarkers (e.g., Von Känel et al., 2011) or
allostatic load (e.g., Song et al., 2013) as physical health outcomes. Only one study conducted by
Kang and Marks (2014) included both individual biomarkers and allostatic load in their study.
The complexity of caregiving experience and the interrelationship among individual biomarkers
may require researchers to employ more diverse biomarkers in a study.
Third, it is widely acknowledged that there is heterogeneity across caregiving subgroups
(e.g., caregiving spouses, caregiving adult-children, caregiving parents). Literature indicates that
they each have a different nature and needs which may lead to differences in their health
outcomes. Given the distinct psychosocial resources represented by each caregiving group, it is
important to distinguish and compare caregiving subgroups. A comparative study of caregiving
subgroups for the effects of wisdom-related personality traits on health will provide evidence
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that can lead to more accurate conclusions in caregiving research, and can thus deepen our
understanding of the relationship between psychosocial factors and health.
Finally, the majority of caregiving research has been conducted by recruiting current
caregivers. Researchers have been relatively less interested in mental and physical health among
former caregivers. Robison-Whelen, Tada, and MacCallum (2001) found that ex-caregivers
reported several emotional and economic troubles after caregiving ceased. Paying attention to
ex-caregivers is important because they may be individuals who need consistent help and follow-
up interventions. Likewise, little is known about positive psychological and physical health
outcomes among ex-caregivers and the relationship between psychological resources and ex-
caregiver s health. Exploring the long-term effects of psychosocial resources on health will
provide evidence regarding whether psychosocial resources facilitate positive health outcomes
even after caregiving has ended. This is also important because it will broaden our understanding
on the positive aspects of caregiving.
To address some of the limitations of previous studies, the current study examined the
role of wisdom-related personality traits on c a r e g i ve rs’ mental and physical health. By applying
the model of resilience and the biopsychosocial approach as theoretical frameworks, the present
study examined the possible direct and indirect effects of wisdom-related personality traits on
caregivers ’ health. To contribute to existing literature on the association between caregiving
experience and caregivers ’ physical health, I included multiple indicators of physical health from
a self-rated measure to diverse physiological biomarkers. Additionally, I distinguished
caregiving subgroups as caregiving spouses, caregiving adult-children, and caregiving parents
and compared them within a single study. To explore the role of wisdom-related personality
traits on individuals who ended their caregiving role, I included ex-caregivers. By examining the
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relationship between psychosocial a c or s a n c a r e g i v e r s health, it may be possible to use this
knowledge to design effective i n e r ve n i ons o i r ove c a r e g i ve r s mental and physical health.
Below, I review the literature on associations between psychosocial resources of resilience and
caregivers ’ health, biopsychosocial approaches in caregiving research, differences in health
outcomes among caregiving subgroups, and the long-term impact of wisdom-related personality
traits on caregivers ’ health, which were used as the basis for the study aims and design.
The Role of Psychosocial Resources of Resilience on Caregivers ’ Health
The study of resilience began in the 1970s focused on maltreated children (Ryff, 2012). A
number of studies have found that some children with mentally ill parents or children of low
socioeconomic status did not have behavior problems and reported relatively better health and
well-being (e.g., Rutter, 1985; Garmezy, 1993). In the 1990s, resilience studies expanded to
diverse populations including older adults experiencing challenges of aging and people
confronting targeted life adversity, such as caregiving (Ryff et al., 2012). Researchers have
investigated what prevents some caregivers from falling ill and helps sustain their well-being
despite their burdens.
Davydov, Stewart, Richie, and Chaudieu (2010) proposed that resilience arises from
complex interactive processes between personal and environmental resources. Herrman, Stewart,
Diaz-Granados, Berger, Jackson, and Yuen (2011) also suggested an interactive model of
resilience illustrates the factors facilitate resilience after experiencing life challenges. They
identified personal (e.g., personality traits) and environmental factors (e.g., social support) as
sources of resilience and suggested that not only direct effects and but also interactive effects
between sources enhance resilience. Based on the resilience model, caregiving researchers
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demonstrated that the direct effects of personal resources and environmental resources,
especially social support, and indirect effects facilitated resilience after experiencing caregiving
(Nijboer, Tempelaar, Triemstra, van den Bos, & Sanderman, 2001; Shirai et al., 2009). These
s u i e s sugg e s ha er so na re sourc e s i re c c o n ribu e o ca re g iv e rs’ e n a l and physical
health, and personal resources indirectly enhance caregiver s’ h e a h hrough increasing support
of the social network (see Figure 1).
Personal Factors of Caregiver Resilience -The MORE Model
Some wisdom researchers suggest that certain resources help people to deal with
challenging life experiences, learn lessons from them, show positive growth, and eventually
attain wisdom (Ardelt, 2005; Glück, 2011). Glück and her colleagues investigated four
interrelated psychological resources that may facilitate development of wisdom through negative
life experiences: a sense of Mastery, Openness to experiences, a Reflective attitude, and Emotion
regulation skills, which are called wisdom-related personality traits in the present study.
A sense of mastery is knowing that one will be able to control and cope with things that
happen to them. Lawton, Kleban, Moss, Rovine, and Glicksman (1989) described caregiving
specific mastery a s “ he osi i ve view o on e ’s a b i i a n on g oing b e ha vi or during the
c a re g ivi n g ro c e ss” . Active mastery is a key predictor of positive growth from stressful
traumatic experiences (Maercker & Zoellner, 2004). Openness to experience is defined as the
tendency to be interested in learning from new perspectives and experiences (Zoellner, Rabe,
Karl, & Maercker, 2008). Reflective attitude refers to ability and willingness to see things from
multiple perspectives (Ardelt, 2005). Emotion regulation is c on ro o one ’ s own e o ions an
perception and management of others ’ emotional reactions. It has been proposed as central to the
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successful management of highly difficult life situation (Gluck, 2011). Given caregiving is a
chronically stressful experience and considered as a life crisis, I expected that the wisdom-related
personality re sour c e s a a c i i a e c a re g ive rs’ r e si ienc e a n help caregivers show more
positive health outcomes.
Among wisdom-related resources, the direct effect of a sense of ma s er on c a re g iv e rs’ mental health has been found throughout the literature. Adams, Smyth, and McClendon (2005)
found that higher levels of personal mastery buffered the relations between stress and depression
among caregivers. Sherwood et al. (2007) also identified perceived mastery as a partial mediator
between the patient behavioral problems and caregiver depression. The impact of a sense of
as e r on c a re g ive rs’ e n a he a h wa s oun in a ong i tudinal study as well (Mausbach et al.,
2007). Furthermore, a se nse o a s er h a s rove n o be a e rson a re sour c e a e c i ng c a r e g iver s’
physical health. Mausbach et al. (2008) suggested that mastery might buffer the relations
between caregiving stress and physiologic measures of cardiovascular and immune system health.
High levels of personal mastery was also associated with less overall fatigue (Roepke et al., 2009)
and less plasma norepinephrine reactivity (less physiological reaction to stress) (Roepke et al.,
2008).
Maercker and Zeollner (2004) argue that people who are more open to new experience
are able to deal better with negative life experiences because they are less afraid of life changes
and new situations. Openness to experience has been positively related with better quality of life
in studies of the general population (Steel, Schmidt, & Shultz, 2008). Tew, Naismith, Pereira,
and Lewis (2013) found that openness to experience was associated with better quality of life
among P a rkinson’ s Disea se ( P D) c a re g ive rs . Several studies demonstrated that openness to
experience predicted mortality risk (Iwasa, Masui, Gondo, Inagaki, Kawaai, & Suzuki, 2008;
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Taylor, Whiteman, Fowkes, Lee, Allerhand, & Deary, 2009; Turiano, Spiro, & Mroczeck, 2012).
These studies demonstrated that higher level of openness to experience was related to decrease in
mortality risk. Although this new line of research connecting openness to experience and
mortality suggests protective effects of openness to experience on physical health, there has been
no research to date exploring the effect of openness to experience on caregivers ’ physical health
using physiological biomarkers.
Reflection is a conscious and growth-oriented process to find benefits or meanings of
experiences (Zoellner & Maercker, 2006). Ardelt (2005) found that reflective attitudes play an
important role in coping successfully with crises a n obs a c es in i n ivi ua s’ i ve s. Ta king multiple perspectives on challenging life events may increase the chance to think about positive
aspects of the events, since it helps people access more possible solutions.
Emotion regulation comprises reappraisal and suppression (Gross, 2001). Reappraisal is
the process by which people change how they think about the situation, and suppression means
that an individual inhibits ongoing emotion expressive behavior (Gross, 1998b). In this review, I
only focus on reappraisal, since emotion regulation as a component of wisdom-related resources
is closer to reappraisal than suppression. C a re g iv e rs’ e o ion re g u a ion s k i s have be e n
considered as protective qualities of caregivers, because people with high emotional regulation
skills are able to recognize and modulate their feelings (Kliewer et al., 2004). This ability helps
caregivers deal with stressors and affect their adjustment. In a study exploring links between
coping and positive psychological states in caregiving experience, positive reappraisal was
associated with positive psychological states during caregiving (Folkman, 1997). One study to
date explored the role of emotion regulation skills on caregivers ’ physical health (Monin, Schulz,
Lemay, & Cook, 2012). They found that using more positive emotion words, which were
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linguistic markers of emotion regulation, was associated with lower cardiovascular reactivity
among caregivers. However, the study has a limitation of using only one type of biomarker
among the diverse physiological indicators of physical health.
Environmental Factors of Caregiver Resilience - Social Support
A positive effect of social support on psychological and physical well-being generally has
been acknowledged and supported by previous research (Ahn, Hochhalter, Moudouni, Smith, &
Ory, 2012; Wilks & Croom, 2008; Piercy et al., 2013). In a review paper, Qualls (2014)
discussed health benefits of positive social relationships and described social mechanisms of how
social support impacts health outcomes. Social support including instrumental and emotional
support benefit health by helping individuals cope with life stressors (Qualls, 2014). Social
support is known as an environmental-systemic factor of resilience, because it may serve to
maintain, protect, and improve health in the face of life challenges (Herrman et al., 2011).
Therefore, social support has been considered one of the key components determining caregivers ’
mental and physical health. Literature on i nks be w e e n s oc i a s u o r a n c a r e g i v e r s health
outcomes has suggested that social support may benefit caregivers ’ health by several ways such
as reducing caregiver stressors, helping to develop effective coping skills, and promoting
positive health behaviors (see for a review, Pinquart & Sörensen, 2007). A number of studies
have shown beneficial effects o soci a su or on ca re g iv e rs’ en a h ealth (Cheng, Lam, kwok,
Ng, & Fung, 2013; Adams et al., 2005; Morlett-Paredes et al., 2014; Yu, Hu, Efird, & McCoy,
2013). Most of these studies have found that participants with larger social networks showed
fewer depressive symptoms, higher life satisfaction and better quality of life. Pinquart &
Sörensen (2007) conducted a meta-analysis of correlates of physical health of informal
caregivers. They used number of chronic illnesses and self-reported health as caregivers ’
17

physical health index and found that lower levels of informal support were associated with
poorer health among caregivers. Williams, Williams, Zimmerman, Munn, Dobbs, and Sloane
(2008) also examined relationship between social support and self-reported health for 434
informal caregivers (94% family). Two studies have explored the role of social support on
caregivers ’ physical health using physiological biomarkers. Lower perception of social support
was associated with reduced immune system sensitivity (Miller, Cohen, & Ritchey, 2002), and
social support can offset stress-induced cardiovascular changes among caregivers (Phillips,
Carroll, Hunt, & Der, 2006). Their results demonstrated that informal social support predicted
better physical health among caregivers.
Indirect Effects of Personal and Environmental Resources of Resilience
In addition to direct effects of personal and environmental resources on c a r e g iv e rs’ health
outcomes, there is a growing interest in indirect effects of these resources (Herrman et al., 2011).
Shirai, Koerner, and Kenyon (2009) examined the direct effects of environmental resources of
resilience and their indirect effects on positive mental health through a personal resource among
caregivers. With data from 63 caregivers, they explored the mediation effect of mastery on a
linkage between social support and caregivers ’ feelings of gain. Social support was measured by
perceived socio-emotional support from each source (family members, friends, spouse/partner).
They found that the direct effect of social support on caregivers ’ feeling of gain was partially
explained by a sense of mastery. However, they did not examine the direct effects and indirect
effects of p e r s ona r e s ou r c e s on c a r e g i ve r s mental health. Moreover, no studies, to our
knowledge, have examined indirect effects of personal and environmental resources of resilience
on caregivers ’ physical health.
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Summary
Taken together, the above findings underscore the role of various psychosocial factors of
resilience in maintaining or regaining health among caregivers. Although recent research
indicates wisdom-related personality traits may be possible personal resources that predict
positive health outcomes of caregivers, the mechanism of how the wisdom-related personality
traits directly and indirectly affect caregivers ’ health is unclear, especially in physical health.
Given the important predictive role of wisdom-related personality traits on caregiver resilience,
understanding direct and indirect pathways through which those resources lead to positive health
outcomes is important.
The Biopsychosocial Model in Caregiving Research
Caregiving studies have tended to be more focused on the demonstrating the linkage
between caregiving and mental health than physical health. However, over the past few decades,
there is growing attention on exploring and understanding the key mechanisms that might
explain how caregiving experience affects physical health outcomes (Roepke et al., 2011). The
biopsychosocial model, an integrative approach including social, psychological, behavioral, and
biological causal mechanisms to health has increasingly become prominent in health outcomes
studies (Crimmins & Seeman, 2004). This theoretical model can be applied to explain an
association between caregiver experience and physical health in caregiving research (Vitaliano,
Zhang, & Scanlan, 2003). This model also helps understand how psychosocial factors have an
effect on caregivers ’ health (Kang & Marks, 2014), and how interactions between factors
produce individual differences in the health outcomes. In sum, the biopsychosocial approach on
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caregivers ’ health can explain resilience in physical health among caregivers, which is
maintenance of good physical health in the face of challenging caregiving experiences.
Self-reported Physical Health
For years, biopsychosocial data collection has dominated by self-reported measures of
physical health (Piazza, Almeida, Dmitrieva, & Klein, 2010). Self-reported general health
perception is one of the most commonly used measures in caregiving health research (Kang &
Marks, 2014; Ourada & Walker, 2014). Self-rated health typically includes a single question that
assesses health from “ e x c e e n ” to “ oor ”. A number of studies have demonstrated the
significant effect of caregiving experience on caregivers ’ self-reported physical health. Cheng
and c o e a g u e s (2013) c o ar e s ouse c a r e g iver s’ se -rated physical health to that of their
non-caregiver peers and found caregivers reported poorer self-rated health than noncaregivers.
Soskolne, Levin, and Yehuda (2007) also found that caregivers reported significantly poorer
health compared to non-caregivers using self-reported health measure as a physical health index.
Several studies have shown that the stronger relationships occurred between caregiving stress
and self-rated health than between stress and biological indicators of health. Kim, Knight, and
Longmire (2007) found that caregiving stress assesse b c a r e r e c i i e n s behavior problems had
a direct effect on the self-rated health, but not on biomarkers (blood pressure levels). In a review
paper, Vitaliano et al. (2003) also found the correlation between caregiving stress and self-rated
health was greater than that for physiological health indicators. They argued that this would
occur since self-rated health is more closely associated with psychological distress caused by
caregiving experience than are physiologic markers.

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Biological Indicators of Health
Although self-reported physical health measures provide important information regarding
the association between psychological and physical health across adulthood, there has been a call
for attending to physiological data in addition to self-reported measures to assess physical health
outcomes (Knight & Sayegh, 2010; Piazza et al., 2010). Biological indicators of health become
increasingly important in research on stress and health in a recent trend of emphasizing
integration of social and biological science (Gruenewald & Kemeny, 2007). Caregiving
experience is believed to be a life challenge that exposes caregivers to stress, and it is widely
acknowledged that stress plays a pivotal role in developing an array of potential problems in
diverse physiological systems. Therefore, caregivers, who are chronically stressed, are at
increased risk for poor physical health. Including clinically assessed health-related biological
markers in a study will strengthen our understanding of the biopsychosocial mechanisms that
might explain how caregiving experience influences physical health.
Previous literature has demonstrated that caregivers experience dysregulation of diverse
physiological systems, which are related to stress response: the sympathetic nervous system
(SNS), the hypothalamic-pituitary-adrenal (HPA) axis, the immune system, and the metabolic
and cardiovascular system.
Circulating or excreted levels of epinephrine (EPI) and norepinephrine (NE) are the
primary biomarkers of SNS activity. Higher levels of both these biomarkers are an indicator of
poorer physical functioning including increased risk of cardiovascular illness and immune
dysfunction (Adler et al., 2002). Cargiving experience has been associated with increased
sympathetic nervous system activity (Irwin et al., 1991; Ho et al., 2014). Using the National
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Survey of Midlife in the United States (MIDUS), Kang and Marks (2014) explored the
association between filial caregiving and physical health outcomes of caregivers. They included
allostatic load, inflammatory dysfunction index, metabolic dysfunction index, and
neuroendocrine dysfunction index as physiological biomarkers. They found that filial caregiving
was associated with greater nueroendocrine dysfunction index which was created by the sum of
four variables including norepinephrine and epinephrine level. Ho et al.(2014) found the role of
reduced activity restriction on relationship between chronic stress and SNS system in that when
activity restriction is high, years of caregiving was significantly associated with EPI.
Whereas SNS activity is associated with immediate response to stress, HPA axis activity
is a longer-term hormone response (Piazza et al., 2010). Corticotrophine-releasing hormone
(CRH), adrenocorticoptropin hormone (ACTH), and dehydroepiandrosterone-sulfate (DHEA-S)
are the primary biomarkers of HPA axis activity. Cortisol is the last downstream hormone that is
synthesized from cholesterol in the adrenal glands in response to signaling messages from CRH
and ACTH synthesized in the hypothalamus and the pituitary gland, respectively, in response to
stress. Normally, cortisol negatively inhibits the hypothalamus from producing more CRH to
slow the system. However, in conditions of chronic (long-time) stress, this negative feedback
loop is not as efficient, leading to dysregulation of the stress-response pathway. Cortisol
secretion is not tightly regulated to return to homeostasis, prolonged periods of cortisol exposure
will lead to prolonged periods of excess circulating glucose, hypertension, and reduced immune
function (for a review, see Klein & Corwin, 2008). HPA axis reactivity to caregiving stressors
has been found in several studies (Brummett et al., 2008; Kim & Knight, 2008; Gallagher-
Thompson et al., 2006; de Vugt, Nicolson, Aalten, Lousberg, Jolle, & Verhey, 2005).
Laudenslager, Sherwood, and Roper (2006) found that caregiving experience affects the
22

re g u a ion o he iurna rh h in sa i va r c or i so . C a r e g i ve r s HPA axis was blunted in that
they failed to show the salivary cortisol rise 30 min following waking. Barker, Greenberg,
Seltzer, and Almeida (2012) examined cortisol patterns in parents of adult children with serious
mental illness. They found that level of cortisol increased less from waking to 30 minute after
waking and decreased less from 30 minute after waking to bedtime among participants. The
results suggested that the chronic stress from long-term parenting of an adult with a disability
decreases physical health status among aging parents.
The immune system is essential to maintain homeostasis in our body. Inflammatory
processes are triggered by immune system, and dysfunction of inflammatory processes is a
central marker of maladaptation to stress. Segerstrom and Miller (2004) suggested that longer
lasting chronic stressors, such as caregiving trigger greater changes in immune system.
Compared to non-caregivers, caregivers showed inflammatory dysfunction in several studies
(Lutgendorf & Laudenslager, 2009; Damjanovic et al., 2007, Mills et al., 2004, Glaser, 2001;
Gallagher et al., 2008; von Känel et al., 2014). Rohleder, Marin, Ma, and Miller (2009)
investigated the changes in inflammatory processes of familial caregivers of patients with brain
cancer for a year after diagnosis. The systemic inflammatory markers, C-reactive protein (CRP)
and interleukin-6 (IL-6) of 18 caregivers were compared to that of 19 non-caregiver controls.
They found that caregivers showed a significant increase in CRP and decrease anti-inflammatory
signaling molecules (mRNA), which suggests inflammatory dysfunction among caregivers. In a
recent study, von Känel and colleagues (2014) found low leisure satisfaction is related to higher
inflammatory markers (tumor necrosis factor- α (TNF- α), IL-8, and interferon(IFG)) among
caregivers.
23

Research also suggests that the risk of overall metabolic and cardiovascular system
dysfunction has been shown in caregivers. Caregivers are at increased risk for CVD and stress-
related cardiovascular impairment (Vitaliano et al., 2003; Grant et al., 2002). Lee, Colditz,
Berkman, and Kawachi (2003) found that high levels of caregiving burden for ill spouses may
increase the risk of coronary heart disease among women caregivers. Von Känel et al. (2011)
showed that Alzheimer ’s disease caregivers had higher cardio-metabolic risk than non-caregivers.
In other study, caregiving experience predicted CVD incidence, and long-term caregiving was
related to double the risk of CVD onset (Capistrant, Moon, Berkman, & Glymour, 2012).
Allostatic Load
Although each biological system operates independently, biological systems also interact
to have an influence on our body. The concept of allostatic load (McEwen, 1998; Sterling &
Eyer, 1988) was introduced in recognition of the need to integrate the effects of stress on the
entire body (Piazza et al., 2010). Allostasis is the process by which the body regulates
physiological responses to accommodate changing environmental demands (Clark et al., 2007;
Sterling & Eyer, 1988). Allostatic load is conceptualized as the cumulative cost or damage that
multiple physiologic systems have endured to maintain allostasis (Roepke et al., 2011).
Allostatic load is assessed by a wide array of biomarkers or multi-system physiological indices.
Several studies have demonstrated the efficacy of allostatic load as a marker of cumulative
biological risk that can predict poorer health (e.g., Seeman, Singer, Singer, Dienberg, & Levy-
Storms, 2002).
In recent years, researchers have become interested in examining the effect of caregiving
experience on physical health measured by allostatic load (Roepke et al., 2011; Kang & Marks,
24

2014, Song et al., 2013). Roepke et al. (2011) found Alzheimer caregivers had significantly
higher allostatic load than non-caregivers. They used an allostatic load index defined as sum of
seven physiological indices including systolic blood pressure, diastolic blood pressure, BMI,
total/HDL cholesterol ratio, HDL cholesterol, plasma norepinephrine, and plasma epinephrine.
Song and colleague (2013) found that the allostatic load of parents of children with
developmental disorders was higher than that of a non-caregiver group. In this study, eleven
biomarkers (systolic blood pressure, diastolic blood pressure, high-density lipoprotein (HDL)
cholesterol, total-to-HDL cholesterol ratio, glycosylated hemoglobin, waist-to-hip ratio, urinary
cortisol, norepinephrine, epinephrine, serum DHEA, and C-reactive protein (CRP)) combined to
create allostatic load. Although these studies have demonstrated the efficacy of allostatic load in
exploring the association between caregiving and physical health among caregivers, they used
different approaches in operationalizing this construct, and there was inconsistency in results
regarding differences in allostatic load score between caregivers and non-caregivers. For
example, Kang and Marks (2014) found that there was not a significant difference in allostatic
load between filial caregivers and non-caregivers. Therefore, more research needs to be
conducted in order to gain a better understanding association between caregiving and allostatic
load level. Furthermore, most previous work has used either single physiological indicator or
allostatic load as physical health outcomes. Therefore, including multiple indicators of physical
health is a useful goal for researchers interested in understanding association between caregiving
experience and physical health.
Summary
Taken together, with growing emphasis on the biopsychosocial model, more studies have
focused on exploring a linkage between caregiver experience and physical health in caregiving
25

research. Biopsychosocial data collection dominated by self-reported measures of physical health
have rapidly expanded to physiological indicators of health and allostatic load. This literature
review on c a r e g i ve r s physical health outcomes suggests that caregivers experience significant
dysregulation of diverse physiological systems. Despite growing importance of biomarker
indices in research on c a r e g i v e r s physical health, fewer studies explored the effects of
psychosocial factors of resilience and their interactions on c a r e g i v e r s physical health assessed by
physiological health indicators. More importantly, previous studies did not use multiple
physiological risk indices in examining the relationship between resi i e nc e a c or s a n c a r e g i ve r s
physical health.
Differences in Health Outcomes among Caregiving Subgroups
Research on mental and physical health outcomes of family caregivers has found
heterogeneity among caregivers. Depending on their family relationship with the care recipient,
each caregiver group (caregiving spouses, caregiving adult-children, and caregiving parents)
reports different obligations and has different caregiver resources available. They also
significantly differ with regard to sociodemographic variables and caregiving-related stressors
(Pinquart & Sörensen, 2011). Corry and While (2009) found that the needs of caregiving spouses
were different from the needs of other family members when caring for someone with multiple
sclerosis. This diverse variability in nature and needs among caregiver groups results in
considerable differences in their health outcomes. Research on people who provided care to a
family member with brain injury demonstrated that the overall perceived health status of
caregiving spouses was lower than caregiving parents (McPherson et al., 2000). Pinquart and
Sörensen (2003) found that differences in stress and depression between caregivers and non-
caregivers was greater for caregiving spouses than for caregiving adult-children. In a meta-
26

analysis, Pinquart and Sörensen (2011) also revealed that caregiving spouses reported poorer
mental health outcomes and greater financial and physical burden than caregiving adult-children
(including children-in-law). Ourada and Walker (2014) compared physical health outcomes of
caregiving parents and caregiving adult-children and found that caregiving parents have poorer
health outcomes than caregiving adult-children. In this study, caregiving parents reported poorer
health outcomes on both self-rated health and number of chronic conditions than caregiving
adult-children. In comparing three groups of caregivers (wives, adult daughters, and mothers
caring for a child with a chronic illness or a disability), Hoyert and Seltzer (1992) found that
patterns of outcomes were similar for caregiving parents and caregiving adult-children while
appearing to manifest in less negative caregiving outcomes than caregiving spouses.
Although there is inconsistency in which group shows better or worse health outcomes,
these comparative studies confirmed that we need to differentiate caregiving subgroups and draw
group specific findings. Previous studies, however, with a few exceptions (Hoyert & Seltzer,
1992; Kang, 2012), compared caregiving experiences between only two groups (e.g., caregiving
spouses vs. caregiving adult-children). Exploring caregiving across a spectrum of family
relationships is important because the processes and consequences of caregiving may differ by
subgroup of caregivers because of their unique resources including sociodemographic and
psychological resources. By identifying the family relationship between the caregiver and the
care recipient, I may provide a more accurate account of the outcomes of caregiving. Based on
the results, appropriate interventions for each caregiver group can be provided as well.
The Effect of the Wisdom-related Personality Traits on Ex-c Health
Most caregiving research has recruited current caregivers and explored the relationship
between psychosocial resources of resilience and caregivers ’ mental and physical health.
27

Although these studies provide valuable insight regarding the positive role of psychosocial
resources on caregiving experience, little is known about whether the effect increases, decreases,
or is stable after the caregiving experience had ceased.
The long-term trajectory of the effects of psychosocial resources on caregivers health can
be expected based on other research areas, such as posttraumatic growth studies. Research on
posttraumatic growth has found that many people reported perceptions of positive growth after
they had experienced negative life events (Calhoun & Tedeschi, 2006). Bluvstein, Moravchick,
Sheps, Schreiber, and Bloch (2013) suggested that posttraumatic growth may attenuate the
negative effect of posttraumatic stress symptoms on mental health. Likewise, certain
psychosocial resources may grow or develop after experiencing caregiving and ameliorate the
stress caused from after caregiving life, and eventually lead to better health outcomes.
Aims and Hypotheses of the Current Study
Based on the resilience model, the first goal of this project (Aim 1) was to examine the
direct and indirect effect of wisdom- re a e e rson a i ra i s on c a re g ive rs’ mental health. In this
study, three variables (a sense of Mastery, Openness to experience, and Emotion regulation)
among the MORE wisdom-related variables were considered as potential caregiver personal
resources affecting caregivers ’ mental health. Reflective attitude was not included in this study
because I could not find any measure that could assess the concept. I predicted that the wisdom-
related personality traits will have direct effects on positive mental health. Specifically, higher
scores on wisdom-related personality traits will result in higher levels of positive mental health
(Hypothesis 1a). I also predicted indirect effects of the personality traits through social support
on caregivers ’ mental health. The positive association between wisdom-related personality traits
28

and positive mental health will be partially mediated by social support (Hypothesis 1b , see
Figure 2 for the conceptual model).
Applying biopsychosocial approach, Aim 2 of the present study was to examine the
association between wisdom-related personality traits and caregivers ’ physical health measured
by both self-rated health and clinically assessed biological risk factors (allostatic load and its
seven subscales). I predicted that higher scores on wisdom-related personality traits will result in
higher levels of self-rated health index
1
(Hypothesis 2a). I predicted that the positive association
between wisdom-related personality traits and caregivers ’ self-rated health will be partially
mediated by social support (Hypothesis 2b). I predicted that higher scores on wisdom-related
personality traits will result in lower levels of physiological indicators of health representing
physiological health risks (Hypothesis 3a). I also predicted that the negative association between
wisdom-related personality traits and caregivers ’ physiological indicators of health will be
partially mediated by social support (Hypothesis 3b , see Figure 3 for the conceptual model).
The next aim of this study was to compare the effects of wisdom-related personality traits
on health among caregiving subgroups. To explain the heterogeneity of caregiving experiences
among caregivers, the present study distinguished caregiving subgroups as caregiving spouses,
caregiving adult-children, and caregiving parents. I did not state a hypothesis regarding the role
of wisdom resources on health among different groups of caregivers for two reasons. First,
empirical work on comparing health outcomes of subgroups is inconsistent. Second, this is the
first study exploring the relationship between wisdom-related personality traits and health among
caregiving subgroups. The health outcomes of each group were also compared to its matched
non-caregivers. I predicted that each caregiver group will show significant relationship between
29

psychosocial resources and mental and physical health outcomes compared to demographically
matched non-caregiving peers (Hypothesis 4).
The final aim of this study was to explore the role of psychosocial resources on ex-
c a r e g i v e r s health. The present study included ex-caregivers who have not provided care for their
family member or friend during the last 12 months but had caregiving experience before. I
r e ic e ro e c i ve e e c s o s c hoso c ia re sou r c e s on e x - c a r e g i ve r s mental and physical
health. Specifically, I predicted that higher scores on wisdom-related personality resources will
result in higher levels of positive mental health among ex-caregivers (Hypothesis 5a). I predicted
an indirect effect of the wisdom resources through social support on ex-caregivers ’ mental health.
The positive association between wisdom-related personality traits and positive mental health
will be partially mediated by social support (Hypothesis 5b). I also predicted that higher scores
on wisdom-related personality traits will result in higher levels of self-rated health among ex-
caregivers (Hypothesis 5c). I predicted that the positive association between wisdom-related
personality traits and ex-caregivers ’ self-rated health will be partially mediated by social support
(Hypothesis 5d). I predicted that higher scores on wisdom-related personality traits will result in
lower levels of physiological indicators of health among ex-caregivers (Hypothesis 5e). I
predicted that the negative association between wisdom-related personality traits and ex-
caregivers ’ physiological indicators of health will be partially mediated by social support
(Hypothesis 5f).

30

Methods
Data
I analyzed data from the study of Midlife in the United States (MIDUS), a longitudinal
study of health and aging in the United States conducted by the MacArthur Foundation Research
Network on Successful Midlife Development. The MIDUS study was initially conducted in
1995-1996 (MIDUS I). The original sample (n = 7,108) was a national probability sample of
non-institutionalized, English speaking midlife adults (age range 25-74) residing in the 48
contiguous states. The sample was composed of four subsamples: (1) individuals selected
through random digit dialing (RDD, n = 3,487), (2) individuals oversampled from 5 urban areas
(n = 757), (3) siblings of individuals from the RDD sample (n = 950), and (4) an RDD sample of
adult twins (n = 1,914). Between 2004-2006, participants (n = 4963, main sample) were asked to
participate in a telephone interview and subsequent postal survey (MIDUS II) similar in content
to MIDUS I. In MIDUS II, additional questions were added in selected areas (e.g., caregiving
experience, cognitive functioning, optimism and coping, and stressful life events). Also, a new
Milwaukee, WI, oversample of African Americans (n = 592) was recruited to participate in a
lengthy field interview and questionnaire paralleling the above instruments for MIDUS II.
Participants who completed the MIDUS II phone interview were re-contacted and asked to
participate in the Biomarker Study (n = 1,255), a Daily Diary Study (NSDE, n = 2,022) and a
Cognitive Study (n = 4512). More detailed information regarding the data set can be found
elsewhere (Dienberg-Love, Seeman, Weinstein, & Ryff, 2010).

31

Participants
Analytic sample for mental health and self-reported physical health analyses. The a na i c s a e or c a r e gi ve r s mental and self-reported physical health study includes a total of
4,963 (main sample) who provided information on caregiving experiences at MIDUS II.
Information about caregiving experiences was asked at MIDUS II but not provided at MIDUS I.
The sample consisted of four subsamples: (1) individuals selected through random digit dialing
(RDD, n = 2,257), (2) individuals oversampled from 5 urban areas (n = 489), (3) siblings of
individuals from the RDD sample (n = 733), and (4) an RDD sample of adult twins (n = 1,484).
Analytic sample for physical health-related biomarker analyses. The Biomarker
Study involved an overnight hospital stay at one of the three general clinical research centers in
the US during which the team performed a detailed health interview, a physical health
examination, the collection of biological specimens, and an additional psychosocial
questionnaire to assess various physiological states (Dienberg-Love et al., 2010). The a na i c s a e or c a r e g i v e r s physical health-related biomarker analyses includes a total of 1,054 (RDD,
n = 640; city oversample, n = 20; siblings, n = 6; and twins, n = 388).
Measures
Wisdom-related Personality Traits.
A Sense of Mastery. A sense of mastery was measured by a 4-item personal mastery
scale. T hi s s c a e e a s ur e s one s sense of efficacy or effectiveness in carrying out goals
(Lachman & Weaver, 1998a). Participants in the survey were asked in the self-administered
questionnaire to indicate the extent of agreement or disagreement with the following statements.
“ I c a n o just about anything I really set my mind to. ”, “When I really want to do something, I
32

usually find a way to succeed at it. ”, “Whether or not I am able to get what I want is in my own
ha n s.” , “ What happens to me in the future mostly depends on me. ” Response categories range
from 1 to 7 (1= Strongly agree; 2 = Somewhat agree; 3 = A little agree; 4 = Neither agree or
disagree; 5 = A little disagree; 6 = Somewhat disagree; 7 = Strongly disagree). Items were
reverse-coded so that high scores reflected higher standing in a sense of mastery. C ronb a c h ’s α
for this scale was .70 (Lachman & Weaver, 1998a).
Openness to Experience. Openness to experience was measured using the survey
questionnaire in which the Big Five personality traits were assessed. Respondents were asked
how much each of following adjectives described them: Creative, Imaginative, Intelligent,
Curious, Broad-minded, Sophisticated, and Adventurous. Items were rated on a 4-point Likert
scale (1= A lot; 2 = Some; 3 = A little; 4 = Not at all). Items were reverse-coded so that high
scores reflected higher openness to experience. C ronba c h’ s α for this scale was .77 (Rossi, 2001).
Emotion Regulation. Emotion regulation was measured by a 4-item positive
reinterpretation and growth scale, which is a subscale of a problem-focused coping index (Carver,
Scheier, & Weintraub, 1989). Items of positive reinterpretation and growth scale were rated on a
4-point Likert scale (1= A lot; 2 = A medium amount; 3 = Only a little; 4 = Not at all). All items
were reverse-coded so that high scores reflected higher standing in the scale. Respondents were
asked “ I r o gr ow a s a e rson a s a re su o he e x e rie nc e .” , “ I r o see i in a i e re n i g h ,
to make i see or e o si ive.” , “ I ook or so e hing g oo in wha i s ha e ning . ” , a n “ I learn
so e hi ng ro he e x e rie nc e . ” C ronba c h ’s α for this scale was .78 (Wrosch, Heckhausen, &
Lachman, 2000).

33

Social Support.
In this study, social support was operationalized as perceived emotional support from the
social network consisting of spouses, family members (except spouse/partner), and friends.
Spouse/partner Support. Spouse/partner support was measured using a 6-item self-
administered questionnaire (Schuster, Kessler, & Aseltine,1990). The spouse/partner support
index indicated the extent of perceived availability of emotional support from spouse/partner.
P a r ici a n s we re a ske : “ How uch oe s ou r s ouse or a r ne r r e a c a r e a b ou ou? ” , “ How uch oe s he o r sh e un e rs a n he w a ou e e abou hi ng s? ” , “ How uch oe s he o r sh e a re c i a e ou? ” , “ How uch o ou r e on hi or he r or he i ou ha ve a s e rious r ob e ? ” ,
“ How uch c a n ou o e n u o hi or he r i ou ne e o a k a bou our w or rie s? ” , a n “ How uch c a n ou r e ax a n be ou rse a roun hi or he r? ” I e s w e re ra e on 4 -point scales (1 =
A lot, 2 = Some, 3 = A little, 4 = Not at all). Items were reverse-coded so that high scores reflect
higher stand on the scale. The scale was computed for cases that have valid values for at least
one item on the sc a e. Cr onba c h’ s a h a or hi s sc a e w a s 90.
Family Support. Family support was measured using a 4-item self-administered
questionnaire (Schuster, Kessler, & Aseltine,1990). The family support index indicated the
extent of perceived availability of emotional support from family (except spouse/partner).
P a r ici a n s we re a ske : “ No inc u ing our spouse or partner, how much do members of your
a i r e a c a re a bou o u? ” , “ How uch o h e un e rs a n he wa o u e e abou hi n g s? ” ,
“ How uch c a n ou r e on he or he i ou ha ve a se rious r ob e ? ” , a n “ How uch c a n
o u o e n u o he i ou ne e o a k abou ou r w or rie s? ” I e s w e re r a e on 4 -point scales
(1 = A lot, 2 = Some, 3 = A little, 4 = Not at all). Items were reverse-coded so that high scores
reflected higher standing on family support. The scale was computed for cases that have valid
34

values for at least one item on the scale. Scores were not calculated for cases with no valid items
on the scale. Cronbac h’ s a ha or hi s sca e wa s . 8 4.
Friend Support. Friend support was also measured using a 4-item self-administered
questionnaire (Schuster, Kessler, & Aseltine,1990). Participants were asked. “ How uch o our
r ien s r e a c a r e a bou o u? ” , “ How uch o h e un e rs a n he wa o u e e abou hi n g s? ” ,
“ How uch c a n ou r e on he or he i ou ha ve a se rious r ob e ? ”, “How much can you
open up to them if you need to talk about your worries? ” Items were rated on 4-point scales (1 =
A lot, 2 = Some, 3 = A little, 4 = Not at all). Items were reverse-coded so that high scores reflect
higher stand on the scale. The scale was computed for cases that have valid values for at least
one item on the scale. Scores were not calculated for cases with no valid items on the scale.
C ronba c h’ s a pha for this scale was .88.
Positive Mental Health.
Life Satisfaction. Satisfaction with life represents a positive mental health outcome in the
present study. Life satisfaction refers o over a a s se ss e n s o one ’s qua i o i e ( Die n e r,
1984). Satisfaction with life was measured by a 5-item self-administered questionnaire.
Respondents were asked to rate their life overall, work, health, relationship with spouse/partner,
and relationship with children ( “On a scale of 0 to 10 where 0 means the worst and 10 means the
best how would you rate your (e.g., satisfaction with work) these days?) (Prenda & Lachman,
2001). The scores for relationship with spouse/partner and relationship with children were
averaged to create on e “ i e ”. T he n, hi s scor e was used along with the remaining three items to
calculate an overall mean score. In the data set, overall mean scores were only recorded. High
scores reflect higher levels of overall life satisfaction. C ronba c h’ s α for this scale was .65.

35

Physical Health.
Self-rated Global Health. Global self-rated physical health was measured using a single
question evaluating respondents ’ physical health. Participants we re a ske : “ I n g e ne ra , wou
you say your physical health is excellent, very good, good, fair, or poor? ” ( 1 = excellent, 2 = very
good, 3 = good, 4 =fair, 5 = poor). Items were reverse-coded so that higher scores reflect better
physical health status.
Allostatic Load. Allostatic load was measured using the sum of activity across seven
separate physiological systems (cardiovascular, lipid, glucose metabolism, inflammation,
sympathetic nervous system (SNS), Hypothalamic pituitary adrenal axis activity (HPA), and
parasympathetic nervous system (PNS)) (Gruenewald et al., 2012). Cardiovascular activity was
measured with resting systolic and diastolic blood pressure (SBP and DBP), and resting pulse
rate. Indicators of lipid and general metabolic activity included body mass index (BMI), waist-
hip ratio (WHR), triglycerides, high density lipoprotein (HDL) cholesterol, and low density
lipoprotein (LDL) cholesterol. Levels of glycosylated hemoglobin, fasting glucose, and the
homeostasis model of insulin resistance(HOMA-IR) served as measures of glucose metabolism.
Inflammation was measured with plasma C-reactive protein (CRP), serum measures of
interleukin-6 (IL-6), fibrinogen, the soluble adhesion molecules e-Selectin, and intracellular
adhesion molecule-1 (ICAM-1). Sympathetic nervous system (SNS) activity was assessed with
overnight urinary measures of epinephrine (EPI) and norepinephrine (NE). Indicators of
hypothalamic pituitary adrenal (HPA) axis activity included 12-hour overnight urinary measure
of the hormone cortisol and serum dehydroepiandrosterone sulfate (DHEA-S). Parasympathetic
nervous system (PNS) activity was assessed with the following heart rate variability parameters:
the standard deviation of R-R (heartbeat to heartbeat) intervals (SDRR), the root mean square of
36

successive differences (RMSSD), and low and high frequency spectral power (LFHRV and
HFHRV). Seven separate physiological systems and representative biomarkers are shown in
Table 1.
Quartile values for each biomarker indicator were calculated to create dichotomous
variables for each indicator where 1= high risk quartile (i.e., high risk = being in the highest
quarter of the distribution for systolic blood pressure, diastolic blood pressure, resting pulse rate,
BMI, waist-hip-ratio, Triglycerides, LDL cholesterol, hemoglobin, fasting glucose, HOMA-IR,
CRP, IL-6, Fibrinogen, sE-Selectin, sICAM-1, epinephrine, norepinephrine, cortisol, and being
in the lowest quarter of the distribution for HDL cholesterol, DHEA, SDRR, RMSSD, low and
high frequency spectral power (LFHRV, HFHRV)) and 0 = otherwise (normal range/low risk).
Descriptive characteristics and distribution on each biomarker and cut points are provided in
Table 1.
Consistent with previous work (Gruenewald et al., 2012), system risk indices were
computed as the proportion of individual biomarker indicators for each system (ranging from 2
to 5 biomarkers) for which participant values fell into high-risk quartile ranges (sum of
dichotomous variable for each indicator was divided by the number of biomarker indicators for
each system). Despite differences between systems in number of biomarkers measured, this
average system scoring method allowed each system risk index to range from 0 to 1. An
allostatic load score was computed as the sum of the seven system scores (possible range: 0 to 7).
System scores were only computed if participants had data on one- ha or ore o he s s e ’s
biomarkers. The multi-system allostatic load score, range 0 to 7, was computed only for
participants who had scores for at least 6 of the 7 systems. The missing system score was
imputed using multiple imputation for 98 participants (83 participants who were missing the
37

Parasympathetic nervous system (PNS) score but had data on all other systems and 15
participants who were missing 1 of the other 6 system scores).
Risk indices of seven physiological systems. Each seven system risk index included to
compute allostatic load was also separately used in this study to asse s s a r i c i a n s individual
system dysfunction (e.g., cardiovascular dysfunction).
Caregiving Status. In the phone questionnaire, the participants were asked if during the
last 12 months they have given personal care for a period of one month or more to a family
member or friend because of a physical or mental condition, illness, or disability at MIDUS II.
R e s on e n s who a nswe r e “ e s” we r e a ske o i n ica e o who he g a v e he os e rsona care (relationship type: husband, wife, son, daughter, father, mother, brother, sister, grandfather,
grandmother, father-in-law, mother-in-law, and other (specify)). They also provided the year
they started caregiving. In this study, caregiving status was coded into multiple categories
including spouse care (n = 114), parent care (including parent-in-law care) (n = 275), child care
(n = 73), and others care (n = 165). Participants who answered “no” on the first question were
asked if they have ever given personal care for a period of one month or more to a family
member or friend because of a physical or mental condition, illness, or disability. Caregiving
status of non-caregivers was then coded into two categories: non-caregivers (n = 3273) and ex-
caregivers (n = 1056). Descriptive characteristics and distribution for all analytic variables by
caregiving status are presented in Table 2 (sample for mental health and self-reported health
analyses only). In physiological biomarker analysis, I used caregiving adult-children (n = 60) for
a caregiver group because two other groups did not have number of participants to perform
analysis (caregiving spouses; n = 24 and caregiving parent; n = 17). Descriptive characteristics
38

and distribution for all analytic variables by caregiving status are presented in Table 3 (sample
for physiological biomarker analysis).
Matched Non-caregivers. Participants in each caregiving group (caregiving spouse,
caregiving adult-children, caregiving parents, ex-caregivers, and caregiver sample for biomarker
analysis) were individually matched with one of 3,273 non-caregivers (one of 700 non-
caregivers in biomarker sample) by using a propensity score matching procedure based on age
and gender
2
. Descriptive χ
2
tests were used to compare caregivers (ex-caregivers) and non-
caregivers on gender, and an independent- gr ou S u en ’ s e s wa s use to compare age. There
were no significant differences in age and gender between each caregiver group (including ex-
caregivers) and the matched non-caregiver group (see Table 4).
Data Analytic Plan
To test the possible direct and indirect effects of wisdom-related personality traits on
caregivers ’ health, total, direct, and indirect effects were calculated. The simple mediation
approach and SPSS macro provided by Preacher and Hayes (2004, PROCESS) were used for this
analysis. The procedure consists of (1) estimating the effect of the wisdom-related personality
traits on changes on the social support (proposed mediator) (a); (2) estimating the effects of
changes in social support on changes in health outcomes, while controlling for the effect of the
wisdom-related personality traits (b); (3) calculating the indirect effect of the effect of the
wisdom-related personality traits on changes in health outcomes through the social support
(proposed mediator) (ab); (4) bootstrapping the sampling distribution of ab and deriving a
confidence interval (CI) with the empirically derived bootstrapped sampling distribution (see
Figure 4). The primary advantage of bootstrapping is greater statistical power without assuming
multivariate normality in the sampling distribution (Preacher & Hayes, 2008). The total effect
39

was defined as the sum of the indirect effect (ab) and direct effect (c') in a given model. Using
the bootstrap sample, the indirect effect (ab) or the product of the two regression coefficients
between wisdom –related personality traits and caregivers ’ health outcomes through social
support (the mediator) was calculated. If the 95% bias-corrected confidence interval for the
parameter estimate did not contain zero, then the indirect effect was statistically significant and
indirect effect was demonstrated (Mallinckrodt et al., 2006; Preacher & Hayes, 2008). More
detailed information about bootstrapping can be found elsewhere (Preacher & Hayes, 2004; 2008;
Shrout & Bolger, 2002).
Based on findings from the literature that social influence on health outcomes varies by
types of social relationship (Brooks, Gruenewald, Karlamangla, Hu, Koretz, & Seeman, 2014;
Robles & Kiecolt-Glaser, 2003), this study differentiated social support by relationship; family
support, friend support, and spouse support. Each type of social support was individually
included in each analysis as a possible mediator.
Results
I b e g in b re s e n i ng he e e c s o wis o - r e a e e r s ona i r a i s on c a r e g i ve r s
mental health by caregiving subgroups (Hypothesis 1 and 4). I then present the effects of the
r a i s on c a r e g i v e r s self-rated health and physical health assessed by biomarkers (Hypothesis 2,
3, and 4). Since I was interested in exploring the difference in the effects of wisdom-related
personality traits on health outcomes between caregivers and non-caregivers, I only reported the
significant direct and indirect effects found among caregivers which were not observed in the
matched non-caregivers. Next, I highlighted characteristics of the effects of the wisdom-related
e rsona i r a i s on non - c a r e g i v e r s health. Lastly, the results of the significant effects of the
40

ra i s on e x - c a r e g i ve r s health which were not observed in the matched non-caregivers were
provided (Hypothesis 5).
The Effects of Wisdom-related Personality T Mental Health
Table 5 presents the results of the effect of wisdom-related personality traits on life
satisfaction through three types of social support among caregiving spouses. The significant
effects not observed in the matched non-caregivers are in boldface. The bootstrap analysis
revealed a direct of effect of openness to experience on life satisfaction among caregiving
spouses: B = .50, SE = .22 (family support). A sense of mastery and emotion regulation
indirectly affected life satisfaction through spouse support: B = .08, SE = .03, 95% CI (.03, .15)
(a sense of mastery) and B = .04, SE = .02, 95% CI (.01, .08) (emotion regulation).
Among caregiving parents, I found that emotion regulation directly affected life
satisfaction compared to non-caregivers: B = .16, SE = .06 (family support). A sense of mastery,
openness to experience, and emotion regulation indirectly affected life satisfaction through social
supports (see Table 6). The effects a sense of mastery on life satisfaction were mediated by
family support, B = .07, SE = .05, 95% CI (.01, .20) and friend support, B = .15, SE = .09, 95%
CI (.03, .38). Openness to experience showed indirect effects on life satisfaction through all three
types of social support: B = .17, SE = .10, 95% CI (.03, .46) (family support), B = .29, SE = .17,
95% CI (.05, .73)(friend support), B = .11, SE = .07, 95% CI (.004, .31)(spouse support). The
effect emotion regulation on life satisfaction was mediated by friend support: B = .09, SE = .04,
95% CI (.01, .18).
41

The effects of wisdom-related personality traits on life satisfaction among caregiving
adult-children were not different from the effects on life satisfaction among the matched non-
caregivers.
The Effects of Wisdom-related Personality Traits on C Self-rated Health
There were no differences in the effects of wisdom-related personality traits on self-rated
health between caregivers and non-caregivers across three caregiving subgroups. All significant
direct and indirect effects of wisdom-related personality traits on self-rated health among
caregivers were also observed in the matched non-caregivers. Moreover, the effects of wisdom-
related personality traits on self-rated health were smaller than the effects on life satisfaction
among caregivers.
The Effects of Wisdom-related Personality Traits on Ca Clinically Assessed
Physical Health
Bivariate correlations for the variables among caregivers in biomarker sample are given
in Table 7. There were no significant correlations between psychosocial resources and physical
health risk indices. I could not find direct or indirect effects of wisdom-related personality traits
on physical health risk indices across three caregiving subgroups.
The Effects of Wisdom-related Personality Traits on N - Health
The direct and indirect effects of a sense of mastery on life satisfaction were found across
three non-caregiver groups. A sense of mastery directly affected self-rated health among non-
caregivers matched to caregiving parents (see Table 8). Among non-caregivers matched to
caregiving adult-children, friend support was an important mediator of the effects of a sense of
42

mastery and openness to experience on self-rated health: B =.03, SE = .01, 95% CI (.005, .06)(a
sense of mastery), B =.08, SE = .04, 95% CI (.01, .18) (openness to experience) (see Table 9).
Table 10 provides bivariate correlations for the variables among non-caregivers matched
to caregivers in biomarker sample. A negative association between a sense of mastery and
Sympathetic Nervous System (SNS) risk index was found (r = -.16). Openness to experience was
negatively related to HPA risk index (r = -.16). Lastly, there was negative relationship between
friend support and PNS risk index (r = -.26). I found some direct and indirect effects of wisdom-
related personality traits on clinically assessed physical health indices (SNS, HPA, and PNS)
among non-caregivers. The bootstrap analysis revealed a direct of effect of a sense of mastery on
SNS risk index: B = -.06, SE = .03 (spouse support) (see Figure 5). Table 11 presents the effects
of wisdom-related personality traits on HPA risk index among non-caregivers. I found a direct
effect of a sense of mastery on HPA among non- caregivers: B = -.06, SE = .02 (family support).
However, a significant positive indirect effect between a sense of mastery on HPA with family
support as the mediator was found: B = .02, SE = .01, 95% CI (.003, .04). Direct effects of
openness to experience on HPA among non caregivers were found: B = -.10, SE = .05 (family
support), B = -.10, SE = .05 (spouse support). I also found a direct effect of emotion regulation
on HPA among non-caregivers: B = -.02, SE = .01 (family support). The indirect effect between
emotion regulation and HPA through family support was also significant in positive direction: B
= .004, SE = .002, 95% CI (.001, .01). Lastly, I found indirect effects of wisdom related
personality traits on PNS among non-caregivers (see Table 12). Friend support was a significant
mediator of the effects of all three wisdom-related personality traits on PNS among non-
caregivers: B = -.03, SE = .02, 95% CI (-.07, -.01) (a sense of mastery), B = -.04, SE = .02, 95%
CI (-.10, -.01) (openness to experience), B = -.01, SE = .01, 95% CI (-.02, -.002) (emotion
43

regulation). This result was in line with the finding that friend support was the only mediator
between wisdom-related personality traits and self-rated health among non-caregivers. The
association between psychosocial resources and other health risk indices (allostatic load,
cardiovascular system, metabolic-lipids, metabolic glucose, and inflammation) were not found
among non-caregivers.
The Effects of Wisdom-related Personality Traits on E - Health
The effects of wisdom-related personality traits on life satisfaction. Bivariate
correlations for the variables among ex-caregivers are given in Table 13. All wisdom-related
personality traits and social supports were related to life satisfaction. Table 14 presents the
results of the effect of wisdom-related personality traits on life satisfaction through three types of
social support. I found significant direct effects wisdom-related personality traits on life
satisfaction among ex-caregivers compared to the matched non-caregivers. First, openness to
experience directly affected life satisfaction: B =.32, SE = .07 (family support) and B =.31, SE
= .07 (spouse support). I also found direct effect between emotion regulation and life satisfaction:
B =.12, SE = .02 (family support), B =.11, SE = .02 (friend support), B =.13, SE = .01 (spouse
support). Openness to experience and emotion regulation showed indirect effects on life
satisfaction. Significant indirect effects between openness to experience on life satisfaction with
social support as the mediator were found: B =.10, SE = .02, 95% CI (.06, .15) (friend support)
and B =.06, SE = .02, 95% CI (.02, .10) (spouse support). The effects of emotion regulation on
life satisfaction were also mediated by social supports: B =.02, SE = .01, 95% CI (.01, .03)
(family support) and B =.01, SE = .004, 95% CI (.001, .02) (spouse support).
44

The effects of wisdom-related personality traits on self-rated physical health. Table
13 shows that all wisdom-related personality traits and social supports were positively correlated
to self-rated physical health among ex-caregivers. Table 15 provides the results of effects of
wisdom-related personality traits on self-rated health through three types of social support among
ex-caregivers. Compared to the matched non-caregivers, direct effects of openness to experience
on self-rated health were found: B =.19, SE = .06 (family support), B =.17, SE = .06 (friend
support), B =.19, SE = .06 (spouse support). I found a direct effect of emotion regulation on self-
rated health: B =.03, SE = .01 (spouse support). Significant indirect effects were also found. The
effects of a sense of mastery on self-rated health were mediated by spouse support: B =.01, SE
= .003, 95% CI (.001, .01) (spouse support). I found significant indirect effects between
openness to experience on self-rated health with social support as the mediator: B =.03, SE = .01,
95% CI (.01, .05) (friend support), B =.01, SE = .01, 95% CI (.002, .03)(spouse support). An
indirect effect between emotion regulation and self-rated health through friend support was also
significant: B =.01, SE = .004, 95% CI (.003, .01).
The effects of wisdom-related personality traits on clinically assessed physical health.
Bivariate correlations for the variables among ex-caregivers in biomarker sample are given in
Table 16. Emotion regulation was positively related to HPA risk index (r = .32). Family support
showed a negative relationship with Metabolic-lipids system risk index (r = -.22), but a positive
correlation was observed with HPA risk index (r = .23). Friend support was negatively related to
Metabolic-lipids (r = -.20) and Metabolic-glucose risk index (r = -.24), but positively associated
with HPA risk index (r = .20). I found some direct and indirect effects of wisdom-related
personality traits on clinically assessed physical health indices (allostatic load, Metabolic-
glucose metabolism, and HPA) among ex-caregivers. Openness to experience directly affected
45

allostatic load among ex-caregivers : B =-.35, SE = .17 (spouse support) (see Figure 6). The
direct and indirect effects of wisdom-related personality traits on metabolic-glucose system were
found among ex-caregivers (see Table 17). I found a indirect effect of a sense of mastery on
metabolic-glucose system through friend support: B =-.02, SE = .01, 95% CI (-.04, -.003). I also
found a direct effect of emotion regulation on metabolic-glucose system: B =-.03, SE = .01
(spouse support). Lastly, I found positive directs effect of emotion regulation on HPA among ex-
caregivers: B =.04, SE = .01(family support), B =.04, SE = .01, (friend support), B =.04, SE
= .01, (spouse support) (see Table 18). The association between psychosocial resources and other
health risk indices (cardiovascular system, metabolic-lipids, inflammation, SNS, and PNS) were
not found among ex-caregivers.
Discussion
The current study examined the effects of wisdom-related personality traits (a sense of
mastery, openness to experience, and emotion regulation) on caregivers ’ mental and physical
health. Based on the resilience model, this study contributed to the understanding of the
relationship between personality traits and caregivers ’ health not only by examining the direct
effects of wisdom-related personality traits but also the indirect effects. The specific aims of the
current study were to (1) examine the direct and indirect effects of wisdom-related personality
ra i s on c a re g iv e rs’ mental health (life satisfaction), (2) explore the association between
wisdom-related personality traits and caregivers ’ physical health measured according to a self-
rated scale and considering clinically assessed biological risk factors, (3) compare those effects
among caregiving subgroups, and (4) explore the role of wisdom-related personality traits in ex-
caregivers ’ mental and physical health.
46

Regarding Aim 1, as predicted, the current study revealed that higher scores on openness
to experience correlated with higher levels of life satisfaction among caregiving spouses
compared to the matched non-caregivers. A sense of mastery and emotion regulation showed an
indirect effect on life satisfaction through spouse support in this group. Among caregiving
parents, emotion regulation directly affected life satisfaction. The effects of a sense of mastery
and emotion regulation were mediated by family support or friend support, and especially
openness to experience indirectly affected life satisfaction through all three types of social
support. Among caregiving adult-children, I did not find significant differences in the effect of
wisdom-related personality traits on life satisfaction between caregivers and the matched non-
caregivers.
Regarding the effects of wisdom-related personality traits on caregivers ’ self-rated health
(Aim 2), the effects were not significantly different between caregivers and non-caregivers
among all three caregiving subgroups. However, mediating effects of social support between
wisdom-related traits and self-rated health were only found with friend support among non-
caregivers matched to caregiving adult-children.
When looking at the association between wisdom-related personality traits and clinically
assessed biological risk indices, there was no direct or indirect effect of wisdom-related
personality traits on biomarkers among caregivers (consisting of only caregiving adult-children).
Unexpectedly, I found some direct and indirect effects among the matched non-caregivers. First,
higher scores on a sense of mastery resulted in lower levels of SNS risk index. Although a sense
of mastery, openness to experience, and emotion regulation showed direct negative effects on
HPA risk index , the effects of a sense of mastery and emotion regulation tended to toward a
positive direction through family support. Lastly, I found indirect negative effects of mastery,
47

openness to experience, and emotion regulation on the PNS risk index through friend support
among non-caregivers.
Regarding Aim 3, the study revealed that the effects of wisdom-related personality traits
on caregivers ’ health varied from caregiving subgroups as reported for Aim 1 and 2.
I investigated the effect of wisdom-related personality traits on health among ex-
caregivers (Aim 4). As predicted, there were significant direct and indirect effects of wisdom-
related personality traits on life satisfaction compared to the matched non-caregivers. Regarding
the effects on self-rated physical health, the personality traits showed direct effects and indirect
effects through social support. Moreover, I found that higher scores on openness to experience
correlated with lower levels of allostatic load among ex-caregivers. I also found a protective
effect of a sense of mastery and emotion regulation on the metabolic-glucose metabolism risk
index. However, higher scores on emotion regulation were associated with higher levels on the
HPA risk index among ex-caregivers.
I began by discussing the findings and their implications relative to the effects of
wisdom-related personality traits on c a r e g i ve r s mental health. I then discussed the results
regarding the effects of wisdom-related personality traits on caregivers ’ self-rated and clinically
assessed physical health. I then focused on the differences in the findings among caregiving
subgroups. Next, I discussed the findings regarding the effects of the traits on non-caregivers. I
then discussed the findings and implications relative to the effects of wisdom-related personality
ra i s on e x - c a r e g i ve r s health. Lastly, I noted the limitations and contributions of this study, and
its overall conclusions.

48

The Effects of Wisdom-related Personality Traits on Caregivers ’ Mental Health
I found some interesting distinctions in the effects of wisdom-related personality traits on
life satisfaction by caregiving subgroups. The results showed that openness to experience played
an important role in life satisfaction among caregiving spouses. This finding is consistent with
Tew et al. (2013) ’s findings that openness to experience was associated with better quality of life
a ong P a rkinson’ s Disea se ( P D) c a re g ive rs. The sample of their study consisted of diverse
caregiving subgroups; however, the highest percentage of the sample was caregiving spouses
(83%). The current finding was intriguing given that I could not find the differences in the
effects of openness to experience on life satisfaction between other caregiver groups and their
matched non-caregivers. One possible reason for this result is age differences between
caregiving spouses and other caregivers. Caregiving spouses were the oldest group (mean age =
63) among the three groups (mean age = 53 (adult-children), mean age = 56 (parents)).
Openness to experience has been demonstrated as a protective factor for mental (Stephan, 2009),
cognitive (Sharp, Reynolds, Pedersen, & Gatz, 2010) , and physical health (Turiano et al., 2012;
Iwasa et al., 2008) for older adults. These studies suggested that openness to experience may act
as a buffer to unique problems and stressors in the lives of older adults because older adults with
high levels of openness to experience are more willing to try new approaches to stress
management (Turiano et al., 2012). Therefore, the current findings suggest that openness to
experience is a beneficial source of caregiver s mental health, especially for older groups, such as
caregiving spouses. Furthermore, this study provides evidence that spouse support is crucial for
better life satisfaction among caregiving spouses. A growing body of research has identified that
a positive relationship between caregivers and care recipients may lead to positive caregiving
experiences for caregivers (Hellstrom et al., 2005a; Shim, Barroso, & Davis, 2012). Moreover,
49

Fergus, Gray, Fitch, Labrecque, and Phillips (2002) emphasized the importance of reciprocal
support and psychological adjustment in patients and caregiving spouses. Another reason for the
importance of spouse support among caregiving spouses is that support from spouses may be the
most available resource for caregiving spouses. Caregiving spouses, due to their advanced age,
are more likely to experience shrinking social networks than other caregivers. Therefore, they
may have less contacts and interactions with other families and friends.
The current study found that scoring higher on emotion regulation has a mental health
advantage over those scoring lower on this trait among caregiving parents. This result may be
due to the fact that caregiving parents relatively spend more years in caregiving for their children
than other caregiving groups (Ourada &Walker, 2014). Actually, caregiving parents have spent
almost double the duration of years (mean = 10.8 years) in caregiving for their children than
other caregiving groups (caregiving spouses = 5.3, caregiving adult-children = 4.8) in this study.
Therefore, caregiving parents often face a commitment of responsibility for many years, and
their stress from caregiving experience is more chronic than others. To adjust to long lasting
cumulative stress, coping strategies such as emotion regulation may be more effective than other
resources. It has been demonstrated in several studies that caregiving parents who use coping
strategies that have focused on problem-solving showed better adjustment in health outcomes
(Abbeduto, Seltzer, Shattuck, Krauss, Orsmond, & Murphy, 2004; Gavidia-Payne & Stoneman,
2006). In addition, Glidden, Billings, and Jobe (2006) found that positive reappraisal which was
the operationalization of emotion regulation in this study was positively related with subjective
well-being. Another interesting finding of this study related to c a re g ivi n g a re n s’ mental health
is that, unlike caregiving spouses, all three types of social support interacted with wisdom-related
personality traits and influenced life satisfaction. The present results are in line with previous
50

studies showing that informal support from family and friends was associated with caregiving
parents ’ well-being (Kaplan, 2010; White & Hastings, 2004; Hastings et al., 2002).
Inconsistent with my hypothesis, there was no difference in the direct effects of a sense of
mastery on life satisfaction between caregivers and the matched non-caregivers. A sense of
mastery conferred some direct effects among all types of caregivers, but, the beneficial effects of
a sense of mastery were also found in the matched non-caregivers. This finding may relate to
several causes. First, the sense of mastery measurement used in this study did not assess
caregiving specific mastery. Rather, it assessed a general sense of mastery over daily life
problems. Therefore, the sense of mastery scale used in this study might measure the level of
mastery in handling daily life events, as opposed to a sense of mastery in dealing with specific
challenges of life related to caregiving. Another possible explanation for this finding is that a
sense of mastery is an effective personal resource on health to broader range of population than
openness to experience and emotion regulation, rather the effects is limited to certain groups
exposed to a high level of stress. This finding suggests that in caregiving research, we should be
cautious when using the sense of mastery measurement and in interpreting the effects of a sense
of mastery. A sense of mastery may be useful personal resources to caregivers, however, future
studies should explore whether the effect of a sense of mastery is related to negative life
experience or stress.
Implications.
The finding that the effects of wisdom-related personality traits vary among caregiving
subgroups suggests that we need to consider different types of interventions for each group. In a
meta-analysis, Sörensen, Pinquart, and Duberstein (2002) found that the effectiveness of
51

interventions differ by caregiving subgroups. Interventions may be effective if we strengthen the
most useful resources for a particular type of caregiver. The beneficial effect of openness to
experience on mental health among caregiving spouses suggests that increasing the level of
openness to experience may be helpful when it comes to improving their life satisfaction.
Improving emotion regulation skills may be an effective intervention for mental health among
caregiving parents. Although there has been some debate on stability of personality over the life
course, recent findings suggest that personality traits can be changed through experience and
intervention (Tang, DeRubeis, Hollon, Amsterdam, Shelton, & Schalet, 2009; Heckman, Pinto,
& Savelyev, 2013). For example, openness to experience seems to change and increase
according to diverse types of interventions including cognitive training (Jackson, Hill, Payne,
Roberts, & Stine-Morrow, 2012). These studies suggested that positive changes in personality
can occur relatively quickly as a result of interventions. We may expect positive mental health
outcomes for caregivers by providing interventions designed for particular types of caregiving.
The findings of the current study also highlight that interventions focused on increasing
social support may not have the same effect on caregiving subgroups. Caregiving spouses may
benefit most from interventions targeted at strengthening the relationship with the care recipient,
although this approach might not be applicable for couples with patients having advanced stage
of physical and cognitive illness. In comparison, caregiving parents may gain most from
interventions increasing social support from diverse relationships.
The Effects of Wisdom-related Personality Traits on Caregivers ’ Self-rated Health
I could not find significant differences in the effects of wisdom-related personality traits
and social supports on c a re g iver s’ self-rated health in three caregiving subgroups. These were
contrasting results related to the effect of these traits on caregivers ’ mental health outcomes.
52

There are two possible explanations for this finding. Pinquart and Sörensen (2003) found that
differences between caregivers and non-caregivers were significantly smaller for physical health
than psychological health outcomes. They suggested that this is because psychological health
outcomes may reflect immediate negative effects of caregiving experience, as opposed to
physical health outcomes, which develop more slowly. Similarly, wisdom-related personality
traits may show positive effects on mental health more quickly than they show effects on
physical health outcomes. An alternative explanation is that the effects of wisdom-related
personality traits and social supports may be weaker in physical health outcomes than in mental
health outcomes. The association between psychosocial resources and physical health may be
weaker than between psychosocial resources and mental health, because those resources are
more likely to correlate with psychological health than physical health. Pinquart & Sörensen
(2007) suggested that social support assessing emotional support is more likely to related to
psychological than physical health. Actually, social support measurements in this study only
measured emotional support.
The Effects of Wisdom-related Personality Traits on Caregivers ’ Clinically Assessed
Physical Risk Factors
I predicted a negative association between psychosocial resources (wisdom-related
personality traits and social support) and caregivers ’ physiological indicators of health. However,
my results did not support this hypothesis. The present result is inconsistent with findings from
previous studies that have shown the association between a sense of mastery and clinically
assessed physical health (Mausbach et al., 2008; Roepke et al., 2008). However, these studies
included dementia caregivers who are known to be the most distressed caregiving group
(Pinquart &Sörensen, 2003). The effects of wisdom-related personality traits may differ
53

according to the level of stress in the caregiving experience. Moreover, my results are not totally
unexpected given the small sample size of the biomarker analysis. The ability to detect small
effects of wisdom-related personality traits may have been reduced due to the small sample size.
Implications.
The findings of this study suggest that we require a larger sample size to understand the
effects of wisdom-related personality traits on c a r e g i ve r s physical health assessed using the
biomarker indices. Moreover, future research should examine these effects among more diverse
groups (e.g., dementia caregivers vs non-dementia caregivers).
Caregiving Subgroups
Consistent with my hypothesis, there were differences in the effects of wisdom-related
personality traits on mental health among caregiving subgroups. The current result suggests that
the association between psychosocial resources and life satisfaction is caregiving specific. I
found certain traits had significant relationships with life satisfaction in certain subgroups. The
effects of social support also differed by caregiving subgroups. In general, I found the highest
number of significant effects of psychosocial resources on life satisfaction among caregiving
parents. I found no difference in the effects of wisdom-related personality traits on life
satisfaction between caregiving adult-children and the matched non-caregivers. One possible
explanation for this finding is that the beneficial effects of resilience resources might be
maximized for caregiving parents because this group is the most vulnerable due to stress
accumulated over a longer period of time, as I mentioned earlier (Hoyert & Seltzer, 1992).
Moreover, in addition to caregiving stress, as parents age, they are likely to deal with multiple
stressors arising from their own age-related changes (Aschbrenner, Greenberg, Allen, & Seltzer,
54

2010). Compared to caregiving parents and spouses, caregiving adult-children spend shorter
periods of time in a caregiving role, spending an average of 5 years caring for their parent
(Seltzer & Seltzer, 1992). Their caregiving roles are also sometimes replaced by a sibling
(Szinovacz & Davey, 2007). Although the literature has been inconsistent with regard to which
group is the most vulnerable to caregiving stress, many studies suggested that spouse caregivers
may experience higher levels of distress than caregiving adult-children (Pinquart & Sörensen,
2011). They suggested that spouses have fewer psychological and physical resources to cope
with stressors due to age-associated losses and decline (Pinquart & Sörensen, 2003; 2011). It is
also plausible that the longer duration of the caregiving experience may give caregiving parents
enough time to develop wisdom-related personality traits through the caregiving experience. The
effect were more likely to be significant after a few years of the caregiving experience.
Implications.
Emerging evidence suggests that associations between psychosocial resources and health
outcomes may differ among caregiving subgroups. The findings of this study confirmed that we
should distinguish the type of caregivers in studies of caregiving. When we combine subgroups,
we may neutralize group-level effects.
The Effects of Wisdom-related Personality Traits on Non-caregivers ’ Health Outcomes
Unexpectedly, I found some direct and indirect effects of wisdom-related personality
traits on mental and self-rated health among non-caregivers. The direct or indirect effects of a
sense of mastery on life satisfaction were found across three non-caregiver groups. And a sense
of mastery directly affected self-rated health among non-caregivers matched to caregiving
parents. Moreover, friend support was the only significant mediator of the effects of a sense of
55

mastery and openness to experience on self-rated health among non caregivers matched to
caregiving adult-children. I already discussed the effects of a sense of mastery on non-caregivers
earlier, in this section, I focused on the importance of friend support on physical health among
non-caregivers.
Emotional friend support has distinct characteristics from family and spouse support.
Friend support is based on friendship defined as a voluntary tie involving affection,
companionship, trust, mutual support, and reciprocity (Blieszner, 2014). To keep friendships,
people need to sustain the mutuality and reciprocity essential to friendship. A friend dealing with
life challenges, such as caregiving is less likely to spend their energy to maintain their
friendships. Therefore, friend support may be more effective and protective resource to non-
caregivers than to caregivers. Some studies suggested the mechanism through which friend
support can facilitate good health. Individuals who have close relationship with their friends are
more likely to share health-related information with friends and help each other comply with
health and medication regimens (Gallan, Spitze, & Prohaska, 2007; Moreman, 2008). Friends
may engage meaningful activities or exercise together which also help maintain good physical
health.
In addition, I did find some beneficial effects of wisdom-related personality and social
support on clinically assessed physical health among the matched non-caregivers. A high level of
sense of mastery was related to lower levels on the SNS and HPA risk indices. In addition,
emotion regulation showed a negative association with the HPA and PNS risk indices. This
finding parallels several studies in which a relationship was identified between wisdom-related
personality traits and physical health among the non-caregiving population (e.g., Morozink,
Fiedman, Coe, & Ryff, 2010). Moreover, by including diverse biomarkers from separate
56

physiological system index to allostatic load, this study demonstrated variations in the strength of
associations between wisdom-related personality traits and physical health. Each trait was
differentially associated with specific physiological system indices. However, the direction of
relationship between social support and physical health in the current study were not consistent
across physical health indices. First of all, I found mediating effects of friend support on PNS
risk index among matched non-caregivers. This is in line with the effects of friend support on
self-rated health. However, unexpectedly, a high level of family support was related to a high
level on the HPA risk index. These findings are partially consistent with Seeman, Gruenewald,
C ohe n , i i a s , a n a he w ( 2014) s study exploring social relationship and their biological
correlates. This study showed relationships between social support and physiological system risk
indices were differ in strength by each individual system. Social support was more strongly
related to certain system index, however, unlike my results, there were the consistency in
direction of relationship between social support and separate physical health index. The
inconsistency in results between two studies might be caused from different study designs in that
Seeman et al. ( 2014) s study did not distinguish subtypes of social support.
It was interesting to find that certain types of social support showed adverse effects on
clinically assessed physical health. Although many studies have demonstrated positive buffering
effects of social support on stressful life events (for a review see Qualls, 2014), a negative effect
of social support on health has also been identified (Rook, 2014). High levels of emotional
support from close relationships may not always indicate positive effects, and may even be a
source of additional stress if the support leads to conflicts, demands, and disappointment.
However, in this study, the negative effects of social support especially from family were only
shown in non-caregivers, not in caregivers. Therefore, social support may have negative effects
57

on physical health among non-caregivers, but, the effect on mental and physical health among a
highly stressed population such as caregivers is still positive.
Implications.
Variations in the strength and directions of associations for specific physiological
systems highlight the complex relationship between psychosocial resources and physical health
among non-caregivers. Friend support may foster physical health among non-caregivers. My
results, however, suggest that social support, especially from close family may confer damage to
physical health among non-caregivers. More research is needed to elucidate the mechanisms
driving not only the positive effects of social support, but also the negative effects of social
support on physical health.
The Effects of Wisdom-related Personality Traits on Health Among Ex-caregivers
This study also provided evidence that the effects of wisdom-related personality traits and
social support lasted even after the caregiving experience had ended. The effects were significant
not only for ex- c a r e g i v e r s mental but also for their physical health. The current study may be the
first comparative study to apply and compare the resilience model to both ex-caregiver and
caregivers; however, one study has c o ar e e x - c a re g ive rs a n non - c a r e g i ve r s mental health
(Rubio, Berg-Weger, Tebb, & Parnell, 2001). They found that the well-being of ex-caregivers
was higher than that of non-caregivers. It appears that the effects of personal and environmental
factors of resilience may be delayed rather than occur immediately. In other words, period of
time may be necessary to maximize the effects. This result agrees with my other finding that
caregiving parents who have spent a relatively longer period of time caregiving than the other
groups showed the greatest effects of wisdom-related personality traits on their mental health
58

outcomes. There are some possible explanations as to why the traits take time to influence
c a r e g i v e r s health. First, it is possible that as caregiving experience continues, the wisdom-
related personality traits develop over time, and the effects become significant consequently.
Roberts, Wood, and Caspi (2008) suggested that personality changes occur when individuals
gradually come to see themselves in a different light, partly a result of taking on new roles and
engaging in the demands of a new environment. The malleability of personality in response to
life changes and challenges in later life has been demonstrated in several studies (Hoerger et al.,
2014; Jackson et al., 2012; Shear & Shair, 2005). For example, Hoerger et al. (2014) found that
caregiving spouses showed changes in their personalities after losing their partner. In this study,
bereaved spousal caregivers were more likely to show an increase in interpersonal orientation
than non caregivers. Several studies also demonstrated that a sense of mastery increased after the
caregiving experience had ended (Mullan, 1992; Skaff, Pearlin, & Mullan, 1996). These studies
suggested the possibility of changes in personality after life challenges and events. Second, as
wisdom researchers suggested, caregivers may gain wisdom through their caregiving experience.
They posit that wisdom-related personality traits may help people to deal with challenging life
experiences and eventually attain wisdom (Ardelt, 2005; Glück, 2011). Therefore, wisdom
developed as a result of caregiving experiences may influence the mental and physical health of
ex-caregivers. Next, regarding the effects of wisdom-related personality on physical health, these
effects may not be immediate and may take time to "get under the skin" and benefit health.
Lastly, the significant effects of wisdom-related personality traits on health among ex-caregivers
may be due to the larger sample size.
As I expected, this study identified the effects of wisdom-related e rsona i on e x -
c a r e g i v e r s clinically assessed biological health risk indices. Openness to experience showed a
59

direct effect on allostatic load. Although their samples did not include ex-caregivers, it has been
demonstrated in several studies that openness to experiences predicts future physical health
especially mortality (Turiano, Spiro, & Mroczek, 2012; Jonassaint et al., 2007). A more recent
study suggested that the health-behavior model (HBM) theory can explain the mechanisms by
which personality traits relate to mortality (Turino, Chapman, Gruenewald, & Mroczeck, 2013).
Adding to the literature that shows positive effects of openness to experience on physical health,
findings from the current study suggest that caregiving experience may strengthen the positive
association between openness to experience and physical health. Efficient skills in providing care
and experiencing negative life events may help people to become more open to new situations
and less afraid of other life changes. Moreover, the changes in openness to experience can
impact health behaviors (e.g., seeking new beneficial health behaviors) and lead to positive
health outcomes. Due to limitations of my cross-sectional data, I could not demonstrate these
changes and their causal effects. Future studies, therefore should use longitudinal data to identify
the relationship.
It is interesting to note that emotion regulation showed both positive and negative direct
effects on clinically assessed physical health indices among ex-caregivers. The high level of
emotion regulation was related to a low level of on the metabolic glucose metabolism risk index,
while the high level of emotion regulation was associated with a high level on the HPA risk
index. Much literature has demonstrated the positive effects of emotion regulation (positive
reappraisal) on psychological and physical health. It has also been suggested that successful
emotion regula i on i s i or a n o c a r e g i v e r s health outcomes (Kliewer et al., 2004; Monin et al.,
2012). However, studies also indicated that individual differences exist in ways of regulating
emotions and some are healthier and more effective than others (Singh & Mishra, 2011). McRae,
60

Ciesielski, and Gross (2012) found that the effect of emotion regulation differs depending on its
goals and tactics. Gross (1998b) mentioned that reappraisal is effective only as long as it is
flexible and realistic. Therefore, the findings of the current study with regard to the effects of
emotion regulation on physical health suggest that there may be less successful emotion
regulation and that the effects on physical health tend to be more complicated than the effects on
mental health. An alternative explanation for this result is that the potential pathway linking
psychosocial resources to physical health might be affected by other unconsidered factors such
as behavioral processes. Uchino (2006) suggested that health behaviors are one possible factor
partially explaining the variance between social support and physical health. He also highlighted
it is important to analyze multilevel pathways to understand complex relationship between
psychosocial resources and physical health. As noted earlier, this is the first study exploring the
e e c s o wis o - r e a e e r s ona i r a i s on c a r e g i ve r s physical health assessed by multiple
physiological system risk indices. Future research is needed to identify similarities and
differences in the role of psychosocial resources on each physiological system. Moreover, we
need more comprehensive view linkage between psychosocial resources and physical health and
consider diverse potential pathway.
Limitations and Contributions
This study has certain limitations that suggest that caution should be used in the
interpretation of its results. First, this study did not include information on the subjective or
objective burden of caregiving (i.e., the level of stress from caregiving) that may impact the
relationship between wisdom-related personality traits and health outcomes. Identifying
c a r e g i v e r s a oun o s r e s s is important because caregiving stress is associated with the
mechanisms by which wisdom-related personality traits are linked to health. I assumed that
61

wisdom-related personality traits, by relieving their stress, would have osi i ve e e c s on
c a r e g i v e r s health. The study could be strengthened if I gathered information on the amount of
burden or stress of caregivers experience. Therefore, future research should test these assumed
mechanisms by including more detailed i n or a i on on c a r e g i v e r s stress levels. Second, the
generailizability of these findings may be somewhat limited, as the majority of participants in the
MIDUS sample were well-educated Caucasians. Third, the small sample size of caregivers in the
biomarker study may underestimate the effect of wisdom-related personality traits on physical
health among caregiving adult-children. By including a larger representative sample, future
studies can determine whether the null finding was caused by the limited sample size. Fourth,
although this study examined some of the most important contributors to health, it is possible
that other important psychosocial factors influence c a r e g i ve r s health outcomes. A greater variety
of variables would be helpful when it comes to understanding c a r e g i ve r s health. Fifth, multiple
analyses performed on each subsample including caregivers and non-caregivers may increase the
overall Type I errors. Lastly, this study used a cross-sectional design, which preludes the ability
to draw causal inferences. Due to this limitation, I could not demonstrate whether caregiving
experience facilitates the development of wisdom-related personality traits, that eventually lead
to positive health outcomes. To confirm this causal ordering, the future study should apply a
longitudinal design. Longitudinally designed studies involving caregiving parents and ex-
caregivers can also demonstrate the long-term impact of wisdom-related personality traits on
c a r e g i v e r s health.
Despite these limitations, this study has a number of strengths and makes a number of
important contributions to the literature on the effects of psych os oc i a r e s o ur c e s on c a r e g i v e r s
health outcomes. First, this study applied the resilience model to explain positive health
62

outcomes in caregiving experience as a result of caregiving experience and found both direct and
indirect effects of resil i e nc e a c or s on c a r e g i v e r s health. Second, the novel contribution of this
study was to extend the literature on the relationship between psycho socia re sourc e s an c a r e g i v e r s health by considering more diverse resources: wisdom-related personality traits.
Third, based on the biopsychosocial model, the inclusion of physical health outcomes assessed
by perceived health and the biomarker health index is another o hi s s u s strong points.
Fourth, this study contributes to a better understanding of c a r e g i ve r s health by differentiating
caregiving subgroups. The study design also allowed us to identify group specific interventions
o i r ove c a r e g i ve r s health outcomes. Fifth, rather than simply reporting the effects of
wisdom-related person a i r a i s on c a r e g i ve r s health, I was able to compare he e e c s on
c a r e g i v e r s health with the effects on non-caregiver control groups. Sixth, I included three types
of social support and analyzed them separately, which allowed me to identify differences in the
r o e o s oc i a s u or i n c a r e g i ve r s health. Lastly, another unique contribution of this study was
that it found the long-term effects of wisdom-related personality traits on c a r e g i ve r s health by
including ex-caregivers.
Conclusion
There have been many important investigations into the effects of personality and social
support on caregive r s health over the last several decades. I have contributed to this literature in
several ways. Unlike prior studies, I applied the resilience model to examine the effects of
personality traits a n s oc i a s u or on c a r e g i v e r s health and included diverse variables:
wisdom-related personality traits.
63

Overall, my study demonstrated the usefulness of wisdom-related personality traits on
caregiver health, and how the effects of these traits differ by caregiving subgroups. Five main
conclusions can be drawn from the current study. First, openness to experience and spouse
support confer protective effects on life satisfaction among caregiving spouses. Second, the
significant predictors of life satisfaction among caregiving parents were emotion regulation and
social support from diverse relationships (spouse, family, and friends). Third, the longer period
of caregiving may lead to the positive e e c s o wi s o - r e a e e r s ona i r a i s on c a r e g i v e r s
mental health. Fourth, the buffering effects o s c hos oc i a r e s ou r c e s on c a r e g i ve r s health were
significant for mental health but not for physical health among caregivers. Fifth, this study
suggests the long-term effects of wisdom-related personality on mental and physical health
among population who had experienced caregiving but do not provide care anymore. We should
continue to question whether changes in wisdom-related personality traits are related to
car e g i v e r s health outcomes. This examination of causal ordering supports the notion that
psychosocial factors are important in determining c a r e g i v e r s health.
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86

Footnote

1
Higher scores in self-rated physical health scale reflect higher standing in physical health
status.

2
I did not consider education and ethnicity because the majority of participants in the
MIDUS II sample were well-educated Caucasians.

87

Table 1. Descriptive characteristics and high-risk cut-points of biomarkers.

System and representative biomarkers n M SD High-risk cutpoint
Cardiovascular
Resting SBP (mmHg) 1053 131.11 17.68 143.00
Resting DBP (mmHg) 1053 75.05 10.25 82.00
Resting pulse rate (bpm) 1052 70.65 11.11 76.00
Metabolic-lipids
BMI 1053 29.18 6.01 32.31
WHR 1052 0.89 0.10 .97
Triglycerides (mg/dL) 1045 135.53 139.75 160.00
HDL Cholesterol (mg/dL) 1043 54.63 17.60 42.00
LDL Cholesterol (mg/dL) 1043 106.31 35.15 128.00
Metabolic-glucose metabolism
Glycosylated hemoglobin (HbA1c) 1040 5.99 0.92 6.12
Fasting glucose (mg/dL) 1039 100.42 24.78 104.00
Insulin resistance (HOMA-IR) 1039 3.33 3.65 4.07
Inflammation
CRP (mg/L) 1040 2.70 4.28 3.19
IL6 (mg/dL) 1044 2.79 2.79 3.17
Fibrinogen (mg/dL) 1040 340.94 83.74 389.75
sE-Selectin (ng/MI) 1044 41.71 20.99 50.58
sICAM-1 (ng/MI) 1044 287.74 100.76 329.42
Sympathetic Nervous System
Urine Epinephrine (ug/g creatine) 1036 2.04 1.30 2.54
Urine Norepinephrine (ug/g creatine) 1042 27.86 13.95 33.33
Hypothalamic Pituitary Adrenal Axis
Urine Cortisol (ug/g creatine) 1051 16.87 26.49 20.00
Blood DHEA-S 1040 105.44 76.21 52.00
Parasympathetic Nervous System
SDRR(msec) 967 34.66 17.16 23.14
RMSSD 967 21.44 17.10 11.67
Low frequency spectral power 967 407.86 593.65 102.50
High frequency spectral power 967 269.00 671.16 51.60
88

Table 2. Descriptive characteristics and distribution for all analytic variables by caregiving status.
(The sample for mental and self-rated health analysis)

Caregiving
Spouses
(n = 114)
Caregiving
adult-children
(n = 275)
Caregiving
Parents
(n = 73)

Non-caregivers
(n = 3273)

Ex-caregivers
(n = 1056)
Variables
M SD

M SD
M SD M SD M SD
Demographics

Age
62.96 11.63 52.81 9.52 55.75 13.09 54.56 12.69 58.12 11.89
Female (%)
65.8 61.5 74.3 47.8 63.0
Education (%)
(High School or greater)
94.7 95.6 85.1 92.5 92.4
Ethnicity (Whites, %)
91.2 89.8 89.2 90.1 91.3
Wisdom-related resources

Sense of Mastery
5.36 1.02 5.54 0.98 5.37 1.01 5.53 0.99 5.55 1.00
Openness to Experiences
2.85 0.51 2.97 0.53 2.99 0.63 2.88 0.53 2.93 0.53
Emotion Regulation
12.95 2.22 12.41 2.25 12.90 2.41 12.22 2.36 12.56 2.37
Social support

Family support
3.31 0.45 3.16 0.49 3.14 0.63 3.26 0.48 3.23 0.49
Friend support
3.21 0.37 3.23 0.39 3.25 0.46 3.2 0.43 3.26 0.44
Spouse support
3.08 0.62 3.21 0.51 3.11 0.76 3.26 0.49 3.26 0.51
Health outcomes

Life satisfaction
7.74 1.21 7.75 1.17 7.33 1.40 7.77 1.24 7.81 1.22
Self-rated health
3.52 1.08 3.54 0.96 3.04 1.16 3.59 1.01 3.44 1.02
89

Table 3. Descriptive characteristics and distribution for all analytic variables by caregiving status.
(The sample for biomarker analysis)

Caregivers
(Adult-children)
(n = 60)

Non-caregivers
(n = 700)

Ex-caregivers
(n = 120)
Variables M SD M SD M SD
Demographics

Age 53.95 9.04 54.17 11.94 57.79 11.39
Female (%) 61.70 48.00 69.00
Education (%) (High School or greater) 100.00 96.00 93.10
Ethnicity (Whites, %)
91.70 93.10 93.10
Wisdom-related resources

Sense of Mastery 5.91 1.04 5.66 0.94 5.65 0.97
Openness to Experiences 3.07 0.50 2.94 0.51 2.97 0.54
Emotion Regulation 12.84 2.28 12.30 2.36 12.48 2.41
Social support

Family support
3.69 0.47 3.28 0.46 3.22 0.54
Friend support
3.41 0.63 3.24 0.42 3.30 0.46
Spousal support
3.67 0.48 3.23 0.52 3.18 0.54
Health outcomes (system risk indices)

Cardiovascular
0.30 0.33 0.27 0.28 0.27 0.27
Metabolic-lipids system
0.27 0.23 0.26 0.26 0.24 0.26
Metabolic-glucose metabolism
0.36 0.37 0.26 0.32 0.31 0.35
Inflammation
0.30 0.25 0.24 0.26 0.27 0.27
Sympathetic nervous system
0.26 0.35 0.27 0.36 0.26 0.37
Hypothalamic pituitary adrenal axis
0.31 0.32 0.25 0.31 0.27 0.31
Parasympathetic nervous system
0.25 0.37 0.28 0.38 0.21 0.37
Allostatic load 2.05 0.97 1.77 1.03 1.88 1.03
90

Table 4. Results of χ
2
tests and t-test comparing age and gender between caregivers and matched
non-caregivers.
Note. Caregivers in biomarker analytic sample only consisted of caregiving adult-children.

Groups Caregivers Non caregivers P value
Spouses

Participants (n)
114 114
Age (years) 62.96 62.29 .642
Female (%)
65.8 65.8 1.000
Adult-children

Participants (n) 275 275
Age (years)
52.81 52.02 .915
Female (%)
61.5 61.6 1.000
Parents

Participants (n)
73 73
Age (years) 55.75 55.21 .844
Female (%)
74.3 72.3 .856
Ex-caregivers

Participants (n)
1056 1056
Age (years)
58.12 57.40 .103
Female (%)
63.0 63.9 .381
Biomarker analytic sample

Participants (n)
60 120
Age (years) 53.95 54.25 .656
Female (%)
61.7 60.7 .885
91

Table 5. The direct and indirect effects of wisdom-related personality traits on life satisfaction
among caregiving spouses.

Note. LS= Life Satisfaction; B= unstandardized coefficient; SE= standard error; CI=confidence interval.
The significant effects not observed in the matched non-caregivers are in boldface.
Analyses control for age, sex, education, and ethnicity.
a
Significant at least at p<.05; Statistical software did not distinguish p-values <.05 for indirect effects.
*p<.05, **p<.001

Direct effect:
Sense of mastery on LS
Indirect effect:
Sense of mastery on LS
B SE B SE 95 % CI
Family support .25* .08 .001 .01 -.02 .04
Friend support .24* .09 .02 .03 -.02 .08
Spouse support .18* .08 .08
a
.03 .03 .15
Direct effect:
Openness to experience on LS
Indirect effect:
Openness to experience on LS
B SE B SE 95 % CI
Family support .50* .22 .01 .04 -.07 .11
Friend support .42 .25 .08 .11 -.12 .31
Spouse support .39 .21 .11 .08 -.03 .29
Direct effect:
Emotion regulation on LS
Indirect effect:
Emotion regulation on LS
B SE B SE 95 % CI
Family support .11 .05 .01 .01 -.001 .04
Friend support .10 .05 .02 .02 -.01 .06
Spouse support .08 .05 .04
a
.02 .01 .08
92

Table 6. The direct and indirect effects of wisdom-related personality traits on life satisfaction
among caregiving parents.
Note. LS= Life Satisfaction; B= unstandardized coefficient; SE= standard error; CI=confidence interval.
The significant effects not observed in the matched non-caregivers are in boldface.
Analyses control for age, sex, education, and ethnicity.
a
Significant at least at p<.05; Statistical software did not distinguish p-values <.05 for indirect effects.
*p<.05, **p<.001

Direct effect:
Sense of mastery on LS
Indirect effect:
Sense of mastery on LS
B SE B SE 95 % CI
Family support .32* .12 .07
a
.05 .01 .20
Friend support .24 .13 .15
a
.09 .03 .38
Spouse support .37* .12 .02 .04 -.03 .13
Direct effect:
Openness to experience on LS
Indirect effect:
Openness to experience on LS
B SE B SE 95 % CI
Family support .43 .27 .17
a
.10 .03 .46
Friend support .31 .27 .29
a
.17 .05 .73
Spouse support .49 .26 .11
a
.07 .004 .31
Direct effect:
Emotion regulation on LS
Indirect effect:
Emotion regulation on LS
B SE B SE 95 % CI
Family support .14 .07 .05 .03 -.01 .13
Friend support .10 .07 .09
a
.04 .01 .18
Spouse support .16* .06 .03 .02 -.002 .09
93

Table 7. Correlation coefficients among key variables for caregiving adult-children in biomarker sample.

Note. OE = Openness to Experience, ER = Emotion Regulation, Cardio = Cardiovascular System, Lipids = Metabolic-lipids System, Glucose = Metabolic-glucose metabolism, IM
=Inflammation, SNS = Sympathetic Nervous System, HPA= Hypothalamic Pituitary Adrenal Axis, PNS = Parasympathetic Nervous System, AL = Allostatic Load
* p<.05 , **p<.001

Mastery OE ER Family
support
Friend
support
Spouse
support
Cardio Lipids Glucose IM SNS HPA PNS AL
A sense of mastery ----
Openness to experience .48** ----
Emotion regulation .33** .43** ----
Family support .05 .10 .38** ----

Friend support .24 -.05 .09 .24 ----
Spouse support .15 -.03 .16 .37** .02 ----

Cardiovascular system .03 .06 -.01 .07 .25 .02 ----
Metabolic-lipids -.20 -.07 -.002 -.12 -.19 .11 .23 ----
Metabolic-glucose -.09 -.03 -.14 -.15 .03 -.08 .30* .36** ----
Inflammation -.20 -.05 -.09 -.07 .09 .01 .18 .27* .27* ----
SNS .13 .19 .09 -.09 .06 -.16 .21 -.10 .13 -.06 ----
HPA .12 .12 .10 -.09 .05 -.14 -.16 -.17 -.10 -.20 .14 ----
PNS -.02 -.15 .08 .04 .02 .20 .16 -.05 .02 .13 -.002 -.07 ----
Allostatic load .04 .09 .11 -.07 .21 -.07 .62** .29* .55** .32* .37** .25 .42** ----
94

Table 8. The direct and indirect effects of wisdom-related personality traits on self-rated physical
health among non-caregivers matched to caregiving parents.
Note. SH= Self-rated health; B= unstandardized coefficient; SE= standard error; CI=confidence interval. Analyses
control for age, sex, education, and ethnicity.
a
Significant at least at p<.05; Statistical software did not distinguish p-values <.05 for indirect effects.
*p<.05, **p<.001

Direct effect:
Sense of mastery on SH
Indirect effect:
Sense of mastery on SH
B SE B SE 95 % CI
Family support .20* .10 .01 .03 -.02 .11
Friend support .20* .10 .01 .02 -.01 .10
Spouse support .19* .10 .02 .02 -.01 .07
Direct effect:
Openness to experience on SH
Indirect effect:
Openness to experience on SH
B SE B SE 95 % CI
Family support -.03 .24 .02 .02 -.01 .07
Friend support -.02 .24 .02 .06 -.04 .21
Spouse support -.004 .24 .01 .03 -.04 .11
Direct effect:
Emotion regulation on SH
Indirect effect:
Emotion regulation on SH
B SE B SE 95 % CI
Family support .04 .05 .01 .01 -.01 .05
Friend support .03 .05 .01 .02 -.01 .06
Spouse support .04 .05 .01 .01 -.004 .03
95

Table 9. The direct and indirect effects of wisdom-related personality traits on self-rated physical
health among non-caregivers matched to caregiving adult-children.
Note. SH= Self-rated health; B= unstandardized coefficient; SE= standard error; CI=confidence interval. Analyses
control for age ,sex, education, and ethnicity.
a
Significant at least at p<.05; Statistical software did not distinguish p-values <.05 for indirect effects.
*p<.05, **p<.001

Direct effect:
Sense of mastery on SH
Indirect effect:
Sense of mastery on SH
B SE B SE 95 % CI
Family support .02 .05 .01 .01 -.01 .04
Friend support -.001 .05 .03
a
.01 .005 .06
Spouse support .02 .05 .00 .003 -.01 .01
Direct effect:
Openness to experience on SH
Indirect effect:
Openness to experience on SH
B SE B SE 95 % CI
Family support .02 .11 .00 .003 -.01 .01
Friend support -.03 .11 .08
a
.04 .01 .18
Spouse support .06 .11 .001 .01 -.01 .03
Direct effect:
Emotion regulation on SH
Indirect effect:
Emotion regulation on SH
B SE B SE 95 % CI
Family support .03 .02 .002 .004 -.002 .01
Friend support .01 .03 .02 .01 .00 .05
Spouse support .03 .02 -.001 .002 -.01 .002
96

Table 10. Correlation coefficients among key variables for non-caregivers matched to caregiving adult-children in biomarker sample.
Note. OE = Openness to Experience, ER = Emotion Regulation, Cardio = Cardiovascular System, Lipids = Metabolic-lipids System, Glucose = Metabolic-glucose metabolism, IM
=Inflammation, SNS = Sympathetic Nervous System, HPA= Hypothalamic Pituitary Adrenal Axis, PNS = Parasympathetic Nervous System, AL = Allostatic Load
The significant association between psychosocial resources and physical health risk indices are in boldface.
* p<.05 , **p<.001

Mastery OE ER Family
support
Friend
support
Spouse
support
Cardio Lipids Glucose IM SNS HPA PNS AL
A sense of mastery ----
Openness to experience .44** ----
Emotion regulation .39** .32** ----
Family support .27** .03 .24** ----

Friend support .34** .23** .33** .36** ----
Spouse support .11 .00 .13 .29** .27** ----

Cardiovascular system .06 .10 .04 -.03 .06 .17* ----
Metabolic-lipids .04 .13 -.06 -.13 -.01 .01 .27** ----
Metabolic-glucose -.04 -.06 -.09 -.11 -.05 .06 .14 .38** ----
Inflammation .05 .01 .002 -.09 -.07 -.07 -.02 .30** .27** ----
SNS -.16* -.06 .01 -.08 .01 .09 .24 .01 .05 .09 ----
HPA -.15 -.16* -.14 .13 -.06 .08 -.01 -.01 .11 -.08 .03 ----
PNS -.10 -.11 -.16 -.13 -.26** .05 .17* .03 .27** .01 .13 .01 ----
Allostatic load -.05 -.10 -.11 -.08 -.08 .12 .52** .41** .49** .29** .46** .24** .54** ----
97

Table 11. The direct and indirect effect of wisdom-related personality traits on Hypothalamic
Pituitary Adrenal Axis among non-caregivers.

Note. SM= A sense of mastery; OE=Openness to experience; ER=Emotion regulation; HPA= Hypothalamic
pituitary adrenal axis; B= unstandardized coefficient; SE= standard error; CI=confidence interval. Analyses control
for age, sex, education, and ethnicity.
a
Significant at least at p<.05; Statistical software did not distinguish p-values <.05 for indirect effects.
*p<.05, **p<.001

Non-caregivers (n = 120)
Social support Direct effect:
SM on HPA
Indirect effect:
SM on HPA
B SE B SE 95 % CI
Family support -.06* .03 .02
a
.01 .003 .04
Friend support -.04 .03 -.004 .01 -.03 .01
Spouse support -.05 .03 .002 .003 -.001 .01
Direct effect:
OE on HPA
Indirect effect:
OE on HPA
B SE B SE 95 % CI
Family support -.10* .05 .002 .01 -.01 .02
Friend support -.10 .05 -.01 .01 -.03 .02
Spouse support -.10* .05 .00 .004 -.01 .01
Direct effect:
ER on HPA
Indirect effect:
ER on HPA
B SE B SE 95 % CI
Family support -.02* .01 .004
a
.002 .001 .01
Friend support -.02 .01 -.002 .004 -.01 .01
Spouse support -.02 .01 .001 .001 -.001 .01
98

Table 12. The direct and indirect effects of wisdom-related personality traits on Parasympathetic
Nervous System (PNS) among non-caregivers.
Note. SM= A sense of mastery; OE=Openness to experience; ER=Emotion regulation; PNS = Parasympathetic
nervous system; B= unstandardized coefficient; SE= standard error; CI=confidence interval. Analyses control for
age, sex, education, and ethnicity.
a
Significant at least at p<.05; Statistical software did not distinguish p-values <.05 for indirect effects.
*p<.05, **p<.001

Non-caregivers (n = 120)
Social support Direct effect:
SM on PNS
Indirect effect:
SM on PNS
B SE B SE 95 % CI
Family support -.03 .03 -.01 .01 -.05 .001
Friend support -.01 .03 -.03
a
.02 -.07 -.01
Spouse support -.04 .03 .002 .004 -.01 .01
Direct effect:
OE on PNS
Indirect effect:
OE on PNS
B SE B SE 95 % CI
Family support -.10 .05 -.004 .01 -.04 .01
Friend support -.06 .06 -.04
a
.02 -.10 -.01
Spouse support -.10 .05 .00 .004 -.01 .01
Direct effect:
ER on PNS
Indirect effect:
ER on PNS
B SE B SE 95 % CI
Family support -.02 .01 -.004 .004 -.02 .001
Friend support -.01 .01 -.01
a
.01 -.02 -.002
Spouse support -.03* .01 .001 .002 -.002 .01
99

Table 13. Correlation coefficients among key variables for ex-caregivers.
Notes. * p<.05 , **p<.001

Mastery Openness to
experience
Emotion
regulation
Family
support
Friend
support
Spouse
support
Life
Satisfaction
Self-rated
physical health
A sense of mastery ----
Openness to experience .34** ----
Emotion regulation .33** .39** ----
Family support .13** .10** .20** ----

Friend support .23** .19** .31** .35 ** ----
Spouse support .12** .11** .06* .15** .14** ----

Life satisfaction .29** .18** .28** .27** .29** .28** ----
Self-rated physical
health
.15** .15** .06* .06* .12** .09** .41** ----
100

Table 14. The direct and indirect effects of wisdom-related personality traits on life satisfaction
among ex-caregivers.
Note. LS= Life Satisfaction; B= unstandardized coefficient; SE= standard error; CI=confidence interval.
The significant effects not observed in the matched non-caregivers are in boldface.
Analyses control for age, sex, education, and ethnicity.
a
Significant at least at p<.05; Statistical software did not distinguish p-values <.05 for indirect effects.
*p<.05, **p<.001

Direct effect:
Sense of mastery on LS
Indirect effect:
Sense of mastery on LS
B SE B SE 95 % CI
Family support .24** .03 .03
a
.01 .01 .05
Friend support .22** .03 .05
a
.01 .03 .07
Spouse support .24** .03 .03
a
.01 .01 .05
Direct effect:
Openness to experience on LS
Indirect effect:
Openness to experience on LS
B SE B SE 95 % CI
Family support .32** .07 .05
a
.02 .02 .10
Friend support .27** .07 .10
a
.02 .06 .15
Spouse support .31** .07 .06
a
.02 .02 .10
Direct effect:
Emotion regulation on LS
Indirect effect:
Emotion regulation on LS
B SE B SE 95 % CI
Family support .12** .02 .02
a
.01 .01 .03
Friend support .11** .02 .03
a
.01 .02 .05
Spouse support .13** .01 .01
a
.004 .001 .02
101

Table 15. The direct and indirect effects of wisdom-related personality traits on self-rated
physical health among ex-caregivers.
Note. SH= Self-rated health; B= unstandardized coefficient; SE= standard error; CI=confidence interval.
The significant effects not observed in the matched non-caregivers are in boldface.
Analyses control for age, sex, education, and ethnicity.
a
Significant at least at p<.05; Statistical software did not distinguish p-values <.05 for indirect effects.
*p<.05, **p<.001

Direct effect:
Sense of mastery on SH
Indirect effect:
Sense of mastery on SH
B SE B SE 95 % CI
Family support .10** .02 .01 .004 -.001 .01
Friend support .09** .02 .01
a
.01 .003 .03
Spouse support .09** .02 .01
a
.003 .001 .01
Direct effect:
Openness to experience on SH
Indirect effect:
Openness to experience on SH
B SE B SE 95 % CI
Family support .19** .06 .01 .01 .00 .03
Friend support .17** .06 .03
a
.01 .01 .05
Spouse support .19** .06 .01
a
.01 .002 .03
Direct effect:
Emotion regulation on SH
Indirect effect:
Emotion regulation on SH
B SE B SE 95 % CI
Family support .03 .01 .004 .003 -.001 .01
Friend support .02 .01 .01
a
.004 .003 .02
Spouse support .03* .01 .002 .001 .00 .01
102

Table 16. Correlation coefficients among key variables for ex-caregivers in biomarker sample.
Notes. OE = Openness to Experience, ER = Emotion Regulation, Cardio = Cardiovascular System, Lipids = Metabolic-lipids System, Glucose = Metabolic-glucose metabolism,
IM =Inflammation, SNS = Sympathetic Nervous System, HPA= Hypothalamic Pituitary Adrenal Axis, PNS = Parasympathetic Nervous System, AL = Allostatic Load
The significant association between psychosocial resources and physical health risk indices are in boldface.
* p<.05 , **p<.001

Mastery OE ER Family
support
Friend
support
Spouse
support
Cardio Lipids Glucose IM SNS HPA PNS AL
A sense of mastery ----
Openness to experience .38** ----
Emotion regulation .28** .48** ----
Family support .27** .16 .21* ----

Friend support .27** .21* .27** .51** ----
Spouse support .19* .19* .15 .35** .19* ----

Cardiovacular system .004 .05 .08 -.09 .04 -.03 ----
Metabolic-lipids .05 -.02 -.10 -.22* -.20* .05 .17 ----
Metabolic-glucose -.11 -.16 -.16 -.18 -.24** .14 .02 .30** ----
Inflammation -.10 -.15 -.15 -.03 -.06 -.04 .12 .25** .25** ----
SNS -.06 -.08 .01 .02 -.06 .08 .10 -.10 .04 .09 ----
HPA -.07 -.01 .32** .23** .20* -.01 .10 -.31** -.14 .11 .14 ----
PNS .07 -.03 -.11 .08 -.06 .12 .14 .06 .18 .27** .10 .09 ----
Allostatic load -.06 -.14 -.02 .02 -.11 .16 .43** .29** .45** .58** .48** .34** .59** ----
103

Table 17. The direct and indirect effect of wisdom-related personality traits on Metabolic-
glucose metabolism(MGM) among ex-caregivers.

Note. SM= A sense of mastery; OE=Openness to experience; ER=Emotion regulation; MGM= Metabolic-glucose
metabolism; B= unstandardized coefficient; SE= standard error; CI=confidence interval.
Analyses control for age, sex, education, and ethnicity.
The significant effects not observed in the matched non-caregivers are in boldface.
a
Significant at least at p<.05; Statistical software did not distinguish p-values <.05 for indirect effects.
*p<.05, **p<.001

Ex-caregivers (n =120)
Social support Direct effect:
SM on MGM
Indirect effect:
SM on MGM
B SE B SE 95 % CI
Family support -.02 .03 -.01 .01 -.03 .01
Friend support -.01 .03 -.02
a
.01 -.04 -.003
Spouse support -.04 .03 .01 .01 -.001 .03
Direct effect:
OE on MGM
Indirect effect:
OE on MGM
B SE B SE 95 % CI
Family support -.08 .06 -.01 .01 -.05 .01
Friend support -.08 .06 -.02 .02 -.06 .001
Spouse support -.11 .06 .02 .01 .00 .06
Direct effect:
ER on MGM
Indirect effect:
ER on MGM
B SE B SE 95 % CI
Family support -.02 .01 -.003 .003 -.01 .002
Friend support -.02 .01 -.01 .004 -.02 .00
Spouse support -.03* .01 .002 .002 -.001 .01
104

Table 18. The direct and indirect effect of wisdom-related personality traits on Hypothalamic
Pituitary Adrenal (HPA) Axis among ex-caregivers.
Note. SM= A sense of mastery; OE=Openness to experience; ER=Emotion regulation; HPA= Hypothalamic
pituitary adrenal axis; B= unstandardized coefficient; SE= standard error; CI=confidence interval.
Analyses control for age, sex, education, and ethnicity.
The significant effects not observed in the matched non-caregivers are in boldface.
a
Significant at least at p<.05; Statistical software did not distinguish p-values <.05 for indirect effects.
*p<.05, **p<.001

Ex-caregivers (n = 120)
Social support Direct effect:
SM on HPA
Indirect effect:
SM on HPA
B SE B SE 95 % CI
Family support -.03 .03 .01 .01 -.01 .04
Friend support -.03 .03 .01 .01 -.01 .03
Spouse support -.02 .03 -.01 .01 -.02 .01
Direct effect:
OE on HPA
Indirect effect:
OE on HPA
B SE B SE 95 % CI
Family support -.05 .05 .01 .01 -.01 .05
Friend support -.05 .06 .01 .01 -.01 .04
Spouse support -.03 .06 -.01 .01 -.05 .01
Direct effect:
ER on HPA
Indirect effect:
ER on HPA
B SE B SE 95 % CI
Family support .04* .01 .001 .003 -.003 .01
Friend support .04* .01 .00 .003 -.01 .01
Spouse support .04* .01 -.002 .002 -.01 .001
105

Figure 1. Possible mechanisms of psychosocial resources predicting caregivers ’ health.

Personal
Resources

Social
Support

Health
Outcomes

106

Figure 2. The conceptual model of the direct and indirect effects of wisdom-related personality
traits on caregivers ’ mental health.

Wisdom-related
Personality
Traits

Social
Support

Positive
Mental Health
+
107

Figure 3. The conceptual model of the direct and indirect effects of wisdom-related personality
traits on caregivers ’ physical health (physiological indicators).

Wisdom-related
Personality
Traits

Social
Support

Physical Health
(physiological
indicators)
_
108

Figure 4. Illustration of direct and indirect effects of wisdom-related personality traits on health.

Note. c = total effect; c' = direct effect; ab = indirect effect.

Wisdom-related
Personality
Traits

Social
Support

Health
Outcomes
c'
b

a
109

Figure 5. The direct effect of a sense of mastery on Sympathetic Nervous System (SNS) among
non-caregivers.

Note. All coefficients represent unstandardized regression coefficients while controlling for age, sex,
education, and ethnicity.
* p<.05 , **p<.001

A Sense of
Mastery

Spouse
Support

Sympathetic
Nervous
System
B = -.06*

B = .054

B = .062
110

Figure 6. The direct effect of openness to experience on allostatic load among ex-caregivers.

Note. All coefficients represent unstandardized regression coefficients while controlling for age, sex,
education, and ethnicity.
* p<.05 , **p<.001

Openness to
Experience

Spouse
Support

Allostatic
Load
B = -.35*

B = .20

B = .20*

Abstract (if available)

Abstract Models of resilience suggest that various psychosocial resources and their interactions facilitate resilience while experiencing life challenges of caregiving. The wisdom-related personality traits have been suggested as possible personal resources of resilience that predict positive health outcomes of caregivers. By applying the model of resilience and the biopsychosocial approach, this study examined the role of wisdom-related personality traits on caregivers’ mental and physical health across caregiving subgroups and ex-caregivers. The aims of this project were to (1) examine the direct and indirect effects of wisdom-related personality traits through social support on caregivers’ mental health (life satisfaction), (2) explore the association between wisdom-related personality traits and caregivers’ physical health measured according to a self-rated scale and multiple biomarkers, (3) compare those effects among caregiving subgroups, and (4) explore the role of wisdom-related personality traits and social support in ex-caregivers’ mental and physical health. Using data from the survey of Midlife in the United States (MIDUS II), the sample for mental health and self-rated physical health analysis consisted of caregiving spouses (n=114), adult-children (n=275), parents (n=73), ex-caregivers (n=1056), and the matched non-caregivers for each caregiving subgroup and ex-caregivers. For analyses of physical health assessed by biomarker indices, caregivers (n=60), the matched non-caregivers (n=120), and ex-caregivers (n=120) were included. The simple mediation model (Preacher & Hayes, 2008) revealed that openness to experience and spouse support were important psychosocial resources related to better life satisfaction among caregiving spouses. I also found that emotion regulation and social support from diverse relationships (spouse, family, and friends) conferred the protective effects on life satisfaction among caregiving parents. There were no differences in the effect of psychosocial resources on life satisfaction between caregiving adult-children and the matched caregivers. Compared to non-caregivers, the effects of wisdom-related personality traits on caregivers̕ physical health were not found in the current study. However, the longer period caregiving (years of caregiving) led to the buffering effects of wisdom-related personality traits on caregivers̕ mental health. Finally, I found direct and indirect effects of wisdom-related personality traits on life satisfaction and self-rated physical health among ex-caregivers. Moreover, openness to experience was negatively related to allostatic load among ex-caregivers. Overall, this study demonstrated that wisdom-related personality traits help caregivers to improve their mental health and showed how the beneficial effects of those traits differ by caregiving subgroups. Findings reveal potential areas for intervention and suggest future directions for research, including the relationship between changes in wisdom-related personality traits and caregivers̕ health outcomes.

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Asset Metadata

Creator Kim, Seungyoun (author)

Core Title The effects of wisdom-related personality traits on caregivers’ health: an application of the resilience model

School Leonard Davis School of Gerontology

Degree Doctor of Philosophy

Degree Program L. Davis School of Gerontology

Publication Date 07/17/2015

Defense Date 05/12/2015

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag caregivers' health,OAI-PMH Harvest,resilience model,wisdom-related personality traits

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Knight, Bob G. (committee chair), Chi, Iris (committee member), Gruenewald, Tara (committee member)

Creator Email seungyok@usc.edu,zenith0326@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-597599

Unique identifier UC11298810

Identifier etd-KimSeungyo-3631.pdf (filename),usctheses-c3-597599 (legacy record id)

Legacy Identifier etd-KimSeungyo-3631.pdf

Dmrecord 597599

Document Type Dissertation

Format application/pdf (imt)

Rights Kim, Seungyoun

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA