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Life transitions, leisure activity engagement, and cognition among older adults
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Life transitions, leisure activity engagement, and cognition among older adults
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1
Life Transitions, Leisure Activity Engagement, and Cognition among Older Adults
Yura Lee
Suzanne Dworak-Peck School of Social Work
Graduate School
University of Southern California
Doctor of Philosophy, Social Work
August 2017
2
Table of Contents
List of Tables .................................................................................................................................. 4
List of Figures ................................................................................................................................. 5
Dedication ....................................................................................................................................... 6
Acknowledgements ......................................................................................................................... 7
Abstract ........................................................................................................................................... 9
Chapter 1: Introduction and Theoretical Framework.................................................................... 11
Background ............................................................................................................................. 11
Theoretical Framework ........................................................................................................... 15
Cognitive Reserve Theory ................................................................................................ 15
Life-Span Development Perspective................................................................................. 16
Stress Buffering Hypothesis ............................................................................................. 17
Purpose of the Study ............................................................................................................... 17
Chapter 2: Life Transitions and Leisure Activity Engagement .................................................... 20
Introduction ............................................................................................................................. 20
Method .................................................................................................................................... 24
Data ................................................................................................................................... 24
Study Sample .................................................................................................................... 25
Measures ........................................................................................................................... 26
Analytic Approach ............................................................................................................ 30
Results ..................................................................................................................................... 32
Characteristics of Retirement Study Sample .................................................................... 32
Multilevel Analysis: Retirement and Leisure Activity Engagement ................................ 33
Characteristics of Widowhood Sample ............................................................................. 34
Multilevel Analysis: Widowhood and Leisure Activity Engagement .............................. 35
Discussion ............................................................................................................................... 36
Impact of Retirement on Leisure Activity Engagement ................................................... 37
Impact of Widowhood on Leisure Activity Engagement ................................................. 38
Conclusion ........................................................................................................................ 40
Appendix 2.A ................................................................................................................................ 53
Appendix 2.B ................................................................................................................................ 55
Chapter 3: Retirement and Cognition among Older Adults: The Role of Leisure Activities ....... 57
Introduction ............................................................................................................................. 57
Method .................................................................................................................................... 61
Data ................................................................................................................................... 61
Study Sample .................................................................................................................... 62
Measures ........................................................................................................................... 63
Data Analysis .................................................................................................................... 67
Results ..................................................................................................................................... 68
Characteristics of Sample by Retirement Status ............................................................... 68
Path Models for Retirement, Leisure Activities, and Cognition ....................................... 70
Testing the Significance of Indirect Effects...................................................................... 71
Discussion ............................................................................................................................... 71
Limitations ........................................................................................................................ 74
Conclusions ....................................................................................................................... 75
3
Chapter 4: Widowhood and Cognition among Older Adults: The Role of Leisure Activities ..... 84
Introduction ............................................................................................................................. 84
Method .................................................................................................................................... 88
Data ................................................................................................................................... 88
Study Sample .................................................................................................................... 89
Measures ........................................................................................................................... 90
Data Analysis .................................................................................................................... 93
Results ..................................................................................................................................... 93
Sample Characteristics ...................................................................................................... 93
Widowhood, Leisure Activity Engagement, and Cognitive Function .............................. 94
Leisure Activities as a Protective Factor .......................................................................... 95
Discussion ............................................................................................................................... 96
Limitations ...................................................................................................................... 100
Conclusions ..................................................................................................................... 100
Chapter 5: Conclusion................................................................................................................. 112
Summary of Research Findings ............................................................................................ 113
Implications for Research ..................................................................................................... 116
Implications for Social Work Practice .................................................................................. 117
References ................................................................................................................................... 119
4
List of Tables
Table 2.1. Subdomains of Leisure Activities from the CAMS ..................................................... 41
Table 2.2. Characteristics of the Retirement Study Sample by Wave .......................................... 43
Table 2.3. Multilevel Model of Retirement and Leisure Activity Engagement ........................... 45
Table 2.4. Characteristics of the Widowhood Study Sample by Wave ........................................ 47
Table 2.5. Multilevel Model of Widowhood and Leisure Activity Engagement ......................... 49
Table 3.1. Subdomains of Leisure Activities from the CAMS ..................................................... 76
Table 3.2. Sample Characteristics by Retirement Status (N = 2,827)........................................... 78
Table 3.3. Indirect Effects of Retirement on Cognitive Function through Leisure Activity ........ 81
Table 4.1. Subdomains of Leisure Activities from the CAMS ................................................... 102
Table 4.2. Sample Characteristics by Widowhood Status (N = 2,618)....................................... 104
Table 4.3. Multiple Regression of Widowhood and Leisure Activities on Cognitive Function 107
Table 4.4. Interaction Models of Widowhood and Leisure Activities on Cognitive Function ... 108
5
List of Figures
Figure 2.1. Sample Selection Criteria for Retirement Study ........................................................ 51
Figure 2.2. Sample Selection Criteria for Widowhood Study ...................................................... 52
Figure 3.1. Sample Selection Criteria for the Present Study ........................................................ 82
Figure 3.2. Path Analysis of Retirement, Leisure Activity Engagement, and Cognition ............. 83
Figure 4.1. Sample Selection Criteria ......................................................................................... 110
Figure 4.2. Mental Activity by Widowhood Status Interactions on Cognitive Function ........... 111
6
Dedication
I dedicate this dissertation to my grandfather and all the family caregivers of dementia
patients.
7
Acknowledgements
I would like to acknowledge many people who supported me throughout the dissertation
process. I want to first mention my grandfather, who motivated me to study older adult
populations with dementia. Without my personal experience as a primary caregiver for my
grandfather, who had Alzheimer’s disease a few years ago, I would not have maintained such a
passion for this population. Thank you and I will always miss you. It was the best part of my life
to spend time with you as your beloved granddaughter.
I would like to express my sincere appreciation to my advisor, Dr. Iris Chi, who gave me
excellent mentorship during my doctoral program. She taught me how to trust my decision-
making process and encouraged me during every part of my learning curve. She always
supported my research ideas and guided me how to develop those ideas into specific research
questions. She was also my life mentor, teaching me how to confront my true identity, move
beyond my limits, and embrace who I really am. Thank you, Dr. Chi, for believing in my
capabilities more than I did and for the confidence you instilled in me.
I would like to also thank my dissertation and qualifying exam committee members, Dr.
Lawrence Palinkas, Dr. Margaret Gatz, Dr. Jennifer Ailshire, Dr. Maria Aranda, Dr. Donald
Lloyd, and Dr. Bob Knight, for guiding me to think through and develop my research questions
and improve my analytic approaches. They also supported me while I was on the job market,
such as writing many thoughtful letters of recommendation and encouraging me in different
ways. Thank you for watching me grow as a researcher with patience and understanding.
I give special thanks to Dr. Michael Hurlburt, Dr. Ann Marie Yamada, and Malinda
Sampson for providing me with emotional support and making consistent efforts to understand
the hardships and concerns of international students with care and an open heart.
8
I am deeply thankful for my family members in South Korea for their unconditional
sacrifice and love. I especially thank my dad for inspiring me with invaluable advice on my
career path; my mom for being the best listener in every circumstance with trust; my
grandmother for pouring out love and care to me through prayers; and my sister for encouraging
me as my life advocator.
I also want to thank my dear cohort members, Adam, Jeremy, Liat, Mee Young, and
Robin, for walking this PhD journey together, being there to console me after my grandfather’s
death, and giving me unforgettable memories of overcoming academic and life struggles
together. Without your company, I would not have survived this program.
My appreciation also goes out to Dr. Hyunyong Park, Lei Duan, and Jun-Tae Park who
helped me with statistical analysis and Eric Lindberg for helping me edit this dissertation.
Last but not least, I would like to acknowledge my good friends Caroline, Claudia,
Hyunju, June-Yung, Jungyeon, Lucy, and Marg. Thank you for developing our friendship,
empowering me in every situation, and helping me walk with God in this country. I am very
grateful to have you as my sisters in Christ.
I believe that every life challenge I went through during my doctoral program was a true
blessing from God. God, thank you for holding my hand and walking me through this
dissertation. Thank you, I love you, and I give you all the glory.
9
Abstract
Maintaining cognitive function is an essential aspect of successful aging because it
enables older adults to retain autonomy and a sense of self into late life. Retirement and
widowhood are the two salient life transitions that can affect older adults’ health, including
cognitive function. However, limited longitudinal studies have examined how retirement and
widowhood influence cognitive function and few of these studies showed equivocal results,
leaving the question of whether any third factor plays a role in this life transitions–cognition
relationship. In another line of inquiry, leisure engagement is receiving increasing attention
because it is still modifiable in later life to help prevent cognitive decline. Moreover, it has been
considered to serve as an adjusting and coping resource relative to various health outcomes (e.g.,
functional limitations, life satisfaction) among individuals who experience significant life events.
Nevertheless, little is known about how leisure activity specifically affects cognitive outcomes
influenced by retirement and loss of a spouse. Thus, this dissertation delineated the mechanism
of life transitions, leisure activity engagement, and cognitive function among older adults using a
national longitudinal data, the Health and Retirement Study (HRS), and its supplementary data,
the Consumption and Activities Mail Survey (CAMS), which repeatedly measured individuals’
leisure activity engagement. Leisure activities were classified into four domains (mental,
physical, social, and household activities) to investigate how specific domains of leisure
activities change over time and influence cognitive function to a distinguishable degree.
The dissertation is organized into three papers. The first paper explored the trajectory of
leisure activity engagement as influenced by retirement and widowhood among older adults. The
second and the third papers investigated the impact of retirement and widowhood on cognitive
function and the role of leisure activity engagement in this relationship.
10
The first study showed that leisure activity engagement significantly decreased during an
8-year period across all four domains of activities. Transitioning from working to retirement
status significantly increased engagement in mental, social, and household activities, whereas
transitioning from married to widowhood status significantly decreased engagement in
household activities. The second study showed that retirement was negatively associated with
cognitive function during a 4-year period. Specifically, individuals who remained retired showed
a significantly lower level of cognitive functioning than those who remained working. Moreover,
engagement in mental activities mediated this negative relationship between retirement and
cognitive function. In particular, the negative impact of retirement on cognitive function was
attenuated by higher levels of mental activity engagement. The third study showed no significant
association between widowhood and cognitive function during a 4-year period. However,
engagement in mental activities moderated the impact of widowhood on cognitive function.
Specifically, the benefit of mental activity engagement on cognition was more pronounced
among individuals who transitioned to widowhood compared to those who remained married.
The findings of this dissertation indicated the protective role of mental activities in the
relationship between retirement or widowhood and cognitive function. This suggests the need for
increased interventions with mentally stimulating activities at the community level (e.g., in
senior centers) to retain cognition among retirees and individuals in early phase widowhood.
Future studies should investigate whether other factors such as socioeconomic status, physical
and mental health, and intergenerational support from adult children after retirement or
widowhood may play a significant role to further influence the mechanism among life
transitions, leisure activities, and cognitive function.
11
Chapter 1: Introduction and Theoretical Framework
Background
Sustaining cognitive function is one of the crucial components of successful aging (Rowe
& Kahn, 1997) because it is among the factors enabling individuals to maintain independence in
later life. Previous studies have shown that better cognitive functioning is associated with higher
psychological well-being (Llewellyn, Lang, Langa, & Huppert, 2008) and social support
(Seeman, Lusignolo, Albert, & Berkman, 2001), whereas cognitive impairment is associated
with greater functional limitations (Stuck et al., 1999), poor health controls (e.g., taking
mediation; Munshi et al., 2006), fall risk (Muir, Gopaul, & Montero Odasso, 2012), and
depressive symptoms (Wilson, Mendes de Leon, Bennett, Bienias, & Evans, 2004) among older
adults.
Retirement and loss of a spouse are the most salient life transitions in later adult life.
Retirement challenges individuals to decide how to allocate time and energy once committed to
work (Nimrod, 2007) and widowhood challenges the surviving partner to adjust to a new life
without a spouse (Patterson, 1996). Indeed, both life events have been shown in previous studies
to have marked effects on physical and mental health outcomes (Dave, Rashad, & Spasojevic,
2006; Li, 2005; Moon, Glymour, Subramanian, Avedaño, & Kawachi, 2012; Stroebe, Schut, &
Stroebe, 2007). However, few studies have examined the influence of these transitions,
specifically on cognitive functioning. Even among existing studies, findings have been
equivocal. For example, some retirement studies have found that retirement has a negative
impact on cognition (Adam, Bonsang, Grotz, & Perelman, 2013; Bonsang, Adam, & Perelman,
2012; Rohwedder & Willis, 2010), whereas other studies found no such effect (Coe, von
Gaudecker, Lindeboom, & Maurer, 2012; Coe & Zamarro, 2011; Roberts, Fuhrer, Marmot, &
12
Richards, 2011). Similarly, among widowhood studies, some studies found that older adults who
lose their spouse show a greater degree of cognitive decline and impairment than their married
counterparts (Aartsen, van Tilburg, Smits, Comijs, & Knipscheer, 2005; Feng et al., 2014;
Håkansson et al., 2009; van Gelder et al., 2006), whereas other studies have shown null (Comijis,
van den Kommer, Minnaar, Penninx, & Deeg, 2011) or negative (Rosnick, Small, & Burton,
2010) associations only among younger men (vs. nonbereaved) or that the negative relationship
disappears when controlling for depression or anxiety (Ward, Mathias, & Hitchings, 2007). Such
equivocal results regarding the relationship between life transitions and cognition suggest the
need to examine whether any third factor plays a role in this relationship.
Leisure activities have been considered to serve as a coping or adjustment resource in the
relationship between life events and health (Coleman & Iso-Ahola, 1993; Kleiber, Hutchinson, &
Williams, 2002) by providing a new role (Bennett, Gibbons, & MacKenzie-Smith, 2010),
reconstruction of meaning of life (Kleiber et al., 2002), and sense of purpose (Silverstein &
Parker, 2002). Indeed, studies have examined the coping role of leisure activities in life events
and various health outcomes such as quality of life (Silverstein & Parker, 2002) and functional
health (Unger, Johnson, & Marks, 1997). However, limited studies have directly examined their
effect on cognitive outcomes specifically. Considering the fact that retirement and loss of a
spouse are both significant life transitions that are likely to affect cognition in later life, leisure
activities may play a pivotal role in helping maintain cognition and better adjusting to life after
retirement or the death of a significant loved one.
In another line of inquiry, considerable research has revealed that individuals with more
years of education (Gatz et al., 2001; Y. Stern et al., 1994), complex occupations (Andel,
Silverstein, & Kåreholt, 2015), and frequent engagement in leisure activities (Podewils et al.,
13
2005; Verghese et al., 2003; J. Y. J. Wang et al., 2006) during mid and late life have less
cognitive decline or lower risk of dementia. Among these predictors, leisure activities, which are
still modifiable in later life, are receiving increasing attention in efforts to decrease the likelihood
of cognitive decline. Some of these studies focused on only one domain of leisure activities
(Laurin, Verreault, Lindsay, MacPherson, & Rockwood, 2001; Wilson et al., 2002), whereas
other studies investigated several domains (e.g., physical, cognitive, social) to examine their
differential effects on cognition (Kåreholt, Lennartsson, Gatz, & Parker, 2011; Verghese et al.,
2003).
Hitherto, little is known about how life transitions affect patterns of engagement in
leisure activities and few longitudinal studies have examined this topic. Relevant leisure studies
in the context of retirement or widowhood were cross-sectional (Patterson, 1996; Şener,
Terzioğlu, & Karabulut, 2007) or measured leisure participation via retrospective memory
(Nimrod, 2007; Rosenkoetter, Garris, & Engdahl, 2001), rather than following the same
individuals over time. The few existing longitudinal studies focused only on one domain (e.g.,
physical or social only) at a time (Evenson, Rosamond, Cai, Diez-Roux, & Brancati, 2002; Utz,
Carr, Nesse, & Wortman, 2002; Wilcox et al., 2003) or combined many activities into summary
measures of general leisure (Iwasaki & Smale, 1998; Janke, Nimrod, & Kleiber, 2008), such that
it remains unclear how specific domains of activities are influenced by life transitions. Moreover,
some of the major studies did not specifically focus on life transitions (Janke, Davey, & Kleiber,
2006), examined only one gender (Gibson, Ashton-Shaeffer, Green, & Autry, 2003; Şener et al.,
2007), or used a non-U.S. sample (e.g., Israel, Australia, Turkey; Nimrod, 2007; Patterson, 1996;
Şener et al., 2007), making it difficult to identify implications for both genders of older
Americans specifically related to life transitions from a longitudinal perspective.
14
Moreover, results from these limited studies have been equivocal. Some studies found
that retirement increased the frequency of involvement in leisure activities (Iwasaki & Smale,
1998), including informal social (e.g., get together with friends, neighbors, or relatives) and
physical activities (Evenson et al., 2002; Janke et al., 2006), whereas other studies reported no
change in physical or social activities after retirement (Rosenkoetter et al., 2001). Similarly,
some studies have shown that widows increased their participation in physical activities (Wilcox
et al., 2003), whereas other studies found that widowed older adults participate in informal social
activities more often than their married counterparts (Utz et al., 2002). Such mixed results among
these studies suggest further exploration of how several domains of leisure engagement are
influenced by retirement and widowhood in the same sample using longitudinal U.S. data.
Thus, the research described in this dissertation first investigated how retirement or loss
of a spouse influences engagement in leisure activities, specifically exploring subdomains of
leisure activities (Study 1). This study further examined the role of leisure activity engagement in
the relationship between life transitions and cognition among older adults (Study 2 and Study 3).
Examining specific domains rather than using a composite measure of leisure activities has two
advantages: (a) it provides better understanding of which properties of leisure activity are
important (Adams, Leibbrandt, & Moon, 2011) for cognitive function among older adults and (b)
it provides a better idea of how to develop intervention programs (e.g., which activities to
include) based on findings. If engaging in leisure activities is a significant adjusting and coping
lifestyle that protects against cognitive decline for this population, implications for quality of life
and readjustment following these transitions can be elucidated.
15
Theoretical Framework
The studies in this dissertation are based on three theoretical constructs: cognitive reserve
theory, life-span development perspective, and stress buffering hypothesis. Either independently
or jointly used in each of these three studies, these theories provide an overall understanding of
how life transitions may influence individuals’ leisure activities and how leisure activities may
serve as adjusting and coping resource in the relationship between life transitions and cognitive
function among older adults.
Cognitive Reserve Theory
Cognitive reserve theory (Scarmeas & Stern, 2003; Y. Stern, 2002) suggests that innate
intelligence and life experiences such as education, occupational attainment, and leisure activity
engagement may improve resilience to brain damage. Thus, individuals with more cognitive
reserve may show fewer cognitive manifestations than their counterparts even with similar brain
pathology. Empirical studies have adopted this theory to examine not only the risk of
neurodegenerative diseases (e.g., Alzheimer’s disease) but also cognitive decline. Indeed, Y.
Stern (2009) contended that “many studies indicate that a set of life experiences such as
educational and occupational exposure and leisure activities are associated with reduced risk of
developing dementia and with a slower rate of memory decline in normal aging” (p. 2016).
Along with other life experiences, leisure activities have been shown to independently contribute
to cognitive reserve and serve as its proxy (Y. Stern, 2009). Indeed, this notion of regarding
leisure engagement as an engaged lifestyle that can benefit cognition is in line with the “use it or
lose it” hypothesis, which posits that continuous use or practice of cognitive skills in activities
may help maintain cognitive performance in later life (Hultsch, Hertzog, Small, & Dixon, 1999).
16
Life-Span Development Perspective
The life-span development perspective conceptualizes how an individual’s behavior
changes or remains static throughout the life course (Baltes, 1987), which may provide a crucial
tool for understanding how older adults increase or decrease their leisure behavior following life
transitions such as retirement or loss of a spouse. The life-span development perspective defines
development as any type of change, regardless of whether it is positive (increase) or negative
(decrease). Three factors have been proposed to influence individuals’ development: normative
age-graded influences, history-graded influences, and nonnormative influences (Baltes, 1987;
Baltes, Reese, & Lipsitt, 1980). Normative age-graded influences are determinants that have a
strong association with chronological age, such that their time of onset is often predictable and
similar for all individuals in a given culture (e.g., family life cycle, education, occupation).
Retirement might be considered as an age-graded influence because it is expected to primarily
occur in later life, although its timing can vary substantially. History-graded influences are
associated with a historical event that similarly affects individuals in the same cohort (e.g., war,
economic depression). Nonnormative influences include significant life events such as
institutionalization, relocation, accidents, and the death of significant loved ones. Although a
greater portion of older people experience widowhood than younger adults, death of a spouse
might be considered a nonnormative influence because the timing and occurrence is not the same
for all individuals and it can unexpectedly happen at any point in the life course. In this
perspective, retirement and widowhood may serve as either or both normative and nonnormative
determinants that affect leisure engagement.
17
Stress Buffering Hypothesis
According to stress buffering hypothesis, coping strategies may protect individuals’
health from stressful life events such as death of a spouse (Cohen & Wills, 1985; Coleman &
Iso-Ahola, 1993). This hypothesis has provided theoretical underpinnings for leisure coping
models and the extensive literature explaining the relationship among leisure, stress, and health
(Kleiber et al., 2002). Leisure activities have been conceptualized as one way of coping after
stressful life circumstances (e.g., loss of a significant other) by generating more social support
(e.g., friendship developed through leisure activities) and providing self-determination (e.g., self-
perception of capacity to initiate actions; Coleman & Iso-Ahola, 1993). Leisure activity
engagement has also been considered to serve as a coping resource relative to negative life
events by providing an opportunity for self-protection, personal transformation, and
reconstruction of life (Kleiber et al., 2002) or a sense of purpose and social integration
(Silverstein & Parker, 2002). This hypothesis provides a good rationale for examining how
leisure engagement may operate as a coping resource in the relationship between life transitions
and cognitive function.
Purpose of the Study
This dissertation is composed of three studies. The first study (Chapter 2) explored how
retirement and widowhood influence leisure activity engagement over time using five waves of
the HRS and CAMS. The second study (Chapter 3) examined how retirement and leisure
activities affect cognitive function and further tested the role of leisure activities in the
relationship between retirement and cognitive function. The third study (Chapter 4) investigated
how widowhood and leisure activities affect cognitive function and further tested the role of
18
leisure activities in the relationship between widowhood and cognitive function. Both the second
and third studies were conducted using three waves of HRS and CAMS data.
This dissertation sought to address some of the research gaps in the literature. First,
previous studies have rarely examined leisure activities as an outcome, instead considering them
as a predictor variable to test their impact on various health outcomes (e.g., life satisfaction,
depression, functioning, survival) among older adults (Adams et al., 2011). Moreover, previous
studies rarely measured duration of engagement in leisure activities, mostly focusing on
frequency or number of activities (Janke et al., 2006; Janke, Nimrod, et al., 2008; Şener et al.,
2007); therefore, little is known about time spent by older adults in various domains of leisure
activities and how this is influenced by life transitions. This may be due to the scarcity of
longitudinal surveys assessing time spent engaged in various activities among older individuals.
Thanks to the release of CAMS data, leisure activity participation measured in terms of time can
be examined over many years. Also, a time measure that provides information on duration may
be useful to distinguish among older individuals who engage in leisure activities at a similar
frequency (e.g., reading books once a week) but invest different amounts of time (e.g., reading
books for 10 hours per week versus 1 hour per week).
Second, a few established studies delineated the positive impact of leisure engagement on
cognitive function (Podewils et al., 2005; Verghese et al., 2003; J. Y. J. Wang et al., 2006) or
dementia risk (Crowe, Andel, Pedersen, Johansson, & Gatz, 2003); however, whether specific
domains of leisure activities affect cognition in the context of life transitions was rarely
examined. Moreover, although some studies examined the role of leisure activities in life events
and health outcomes (e.g., quality of life, functional health; Silverstein & Parker, 2002; Unger et
al., 1997), limited studies have examined their effect on cognitive outcomes specifically. Even
19
the few studies on cognitive function have largely tested the role of leisure activity in relation to
genetic (e.g., ApoE4 allele; Niti, Yap, Kua, Tan, & Ng, 2008), early life (e.g., education;
Lachman, Agrigoroaei, Murphy, & Tun, 2010), and midlife (e.g., occupational complexity;
Andel, Silverstein, et al., 2015) factors, but not later-life transitions such as retirement or loss of
a spouse.
Third, previous relevant leisure studies have often focused on widowed or retired-only
samples (Janke, Nimrod, et al., 2008; Şener et al., 2007), making it difficult to compare findings
to nonretired or nonbereaved individuals. Even those studies comparing nonretired vs. retired or
nonbereaved vs. bereaved samples, the implication was less clear for individuals in the midst of
transitioning to retirement or widowhood. Including a comparison group of transitioning
individuals may explicate not only long-term effects but also short-term effects of retirement and
widowhood.
20
Chapter 2: Life Transitions and Leisure Activity Engagement
Introduction
One of the crucial aspects of successful aging is active engagement in later life (Rowe &
Kahn, 1997). Not surprisingly, an established body of research has shown the positive
association between leisure engagement and health outcomes among older adults (Adams et al.,
2011). Indeed, later-life engagement in leisure activities was found to be related to higher levels
of self-rated health (Morrow-Howell et al., 2014) and quality of life (Silverstein & Parker, 2002)
and lower levels of functional limitations (Janke, Payne, & Van Puymbroeck, 2008), depressive
symptoms (Glass, Mendes de Leon, Bassuk, & Berkman, 2006; Hong, Hasche, & Bowland,
2009; Morrow-Howell et al., 2014), mortality (Agahi, Silverstein, & Parker, 2011), and cognitive
impairment (J. Y. J. Wang et al., 2006).
Retirement and widowhood are two significant life transitions in later life that may
largely influence leisure engagement patterns among older adults. Retirement challenges retirees
to decide how to use or allocate a great amount of time and energy previously dedicated to work
(Nimrod, 2007). Widowhood, considered the most distressing event in later life (Fry, 2001),
challenges surviving partners to replace their spousal roles through lifestyle changes (Pienta &
Franks, 2006) and multifaceted postbereavement adaptations (Carr & Utz, 2001). Although
retirement may not be considered as stressful as widowhood, it certainly entails detachment from
available resources, social networks, and identities linked to a major career and job (Kim &
Moen, 2002). Such interruption in the sense of self can cause significant behavioral changes
(Bridges, 2004) among retired individuals. In this respect, retirement and widowhood may serve
as important factors that influence leisure behaviors among older adults during the adjustment to
labor force exit or the death of a spouse.
21
From a theoretical perspective, three classic social gerontology theories have often been
used to explain leisure activity engagement and well-being in later life. Activity theory posits
that older individuals substitute new activities for their lost social roles and age-related declines
(Friedman & Havighurst, 1954; Havighurst, 1963). Continuity theory (Atchley, 1989) states that
individuals’ habits and preferences are likely to persist throughout the life course.
Disengagement theory (Cumming & Henry, 1961) assumes that older persons tend to withdraw
from society or the environment in which they are situated. Although these theories may explain
an individual’s activity participation associated with well-being, they may not be sufficient when
it comes to examining activity participation as a major outcome directly influenced by life
transitions (e.g., retirement or widowhood). Indeed, Utz et al. (2002) argued that “despite
activity, continuity, and disengagement theories’ inimitable presence in social gerontology, their
explanatory power has fallen short in trying to explain how or why older adults alter their social
participation in the face of widowhood” (p. 531) and further suggested adopting the life-span
perspective (Baltes et al., 1980) to capture the progressive nature of the human life course.
The life-span development perspective (Baltes, 1987) emphasizes the interplay between
gain and loss in individual development, thereby focusing on both “constancy and change in
behavior throughout the life course” (p. 611). This perspective introduces normative age-graded
events that tend to occur in similar ways for all individuals with regard to chronological age
(e.g., family cycle, occupation) and nonnormative significant life events that may not be
predictable in time and occurrence (e.g., relocation, accidents, death of significant others) as two
of the three major systems that influence individuals’ behavior in life-span development (Baltes
et al., 1980). In this respect, retirement and widowhood may serve as either or both normative
and nonnormative events that influence an individual’s leisure participation. Hence, the life-span
22
perspective becomes especially important to examine changes in older adults’ leisure
participation over time in relation to life transitions (Janke et al., 2006).
Hitherto, limited empirical findings exist regarding the impact of life transitions on
leisure activity participation among older adults from a longitudinal perspective. Previous studies
have often used cross-sectional data and focused on retired or widowed individuals only
(Patterson, 1996; Rosenkoetter et al., 2001; Şener et al., 2007), making it difficult to understand
whether results regarding leisure activities were largely influenced by life transitions or just
reflecting the characteristics of retired or widowed individuals. In addition, some studies relied
on a retrospective measure to define changes in leisure activities (Nimrod, 2007; Rosenkoetter et
al., 2001) instead of using a repeated measure of leisure activity participation, which may have
generated recall bias. Moreover, leisure engagement was largely treated as a predictor variable in
testing its impact on older individuals’ well-being (e.g., life satisfaction, depression; Adams et
al., 2011) rather than as an outcome, leaving predictors of leisure participation among older
individuals largely unexplored. Even the limited longitudinal studies that assessed leisure
engagement as an outcome measured these activities using a general summary variable (Iwasaki
& Smale, 1998; Janke, Nimrod, et al., 2008) or focused on one domain of leisure activities (e.g.,
physical or social only) at a time (Berger, Der, Mutrie, & Hannah, 2005; Evenson et al., 2002;
Lahti, Laaksonen, Lahelma, & Rahkonen, 2011; Utz et al., 2002; Wilcox et al., 2003), making it
difficult to understand how specific domains of leisure activities are influenced by retirement and
widowhood to a distinguishable degree. In addition, some studies examined only one gender
(Gibson et al., 2003; Şener et al., 2007), used a non-U.S. sample (e.g., Israel, Australia, Turkey,
Netherlands; Koeneman et al., 2012; Nimrod, 2007; Patterson, 1996; Şener et al., 2007), or used
23
a key predictor other than life transitions (Janke et al., 2006). Therefore, the implications for both
genders among older Americans who transitioned to retirement or widowhood remain unclear.
Moreover, findings of the limited relevant studies on retirement and widowhood have
been equivocal. For example, Janke et al. (2006) found that physical and informal social (e.g.,
talking on the phone or getting together with friends, neighbors, or relatives) activity increases as
individuals transition from work to retirement. Rosenkoetter et al. (2001) found no change in
physical or social activities after retirement. Koeneman et al. (2012) found that retired
individuals increased their time spent engaged in physical activities compared to their working
counterparts. On the other hand, Berger et al. (2005) found that a majority of their sample
reported a slight increase in time spent engaged in leisure physical activities after retirement, but
this was not sufficient to compensate for lost physical activity through work. Regarding
widowhood, Utz et al. (2002) found that widowed individuals participated more in informal
social activities (e.g., phone contact with friends, relatives) than their married counterparts,
whereas Wilcox et al. (2003) noted increased physical activity participation among widowed
individuals (vs. those who remained married). Such equivocal results leave unanswered the
question of how various domains of leisure activities change over time in the same sample,
specifically as influenced by retirement and widowhood.
Thus, this study examined changes in different domains of leisure activity engagement
during an 8-year period as influenced by retirement and widowhood using five waves of national
panel data from the HRS and its supplementary survey, CAMS. Although previous studies have
often explored frequency of engagement (e.g., never to daily) or overall number of leisure
activities (Janke et al., 2006; Janke, Nimrod, et al., 2008; Rosenkoetter et al., 2001; Şener et al.,
2007; Strain, Grabusic, Searle, & Dunn, 2002), the present study measured weekly time spent
24
engaged in leisure activities. Until the release of CAMS, no U.S. longitudinal data measuring
actual time spent on leisure activities among older adults were available. The advantage of
measuring time engaged in leisure participation is that it considers possible variability among
individuals with a similar frequency level of leisure engagement. For example, two individuals
who report reading every day may invest a distinguishable amount of time in reading (e.g., one
may spend 5 hours per day whereas the other spends 1 hour per day). Hypotheses were not posed
for this study due to the dearth of previous relevant studies. Instead, three research questions
guided the study.
Research question 2.1. How do different domains of leisure activities change during an
8-year period?
Research question 2.2. How does retirement influence individuals’ leisure activity
engagement during an 8-year period?
Research question 2.3. How does widowhood influence individuals’ leisure activity
engagement during an 8-year period?
Method
Data
The HRS and its supplementary data, CAMS, were used for the present study. The HRS
is a nationally representative panel survey of older adults aged 51 and older in the United States.
Beginning in 1992, the HRS involved biennial interviews with respondents, gathering
information on family structure, employment, and health. The HRS used a stratified, multistage
area probability sample design with oversampling of African Americans, Hispanics, and
Floridians. Detailed information about the study is available elsewhere (Juster & Suzman, 1995).
25
During the years between HRS interviews, a random subsample of the HRS was
interviewed to collect CAMS data, including information about time spent on various activities,
household consumption, and prescription drug use (Hurd & Rohwedder, 2009). In 2001, the
initial wave of CAMS was conducted with a random subsample of 5,000 households that
participated in the HRS 2000 survey. If a household had two eligible respondents, only one
respondent was chosen to participate in 2001 and 2003 (Hurd & Rohwedder, 2007).
From 2005, the CAMS sample was configured differently from prior waves. If a
household had two eligible participants, both individuals were included in the sample, unlike the
prior waves, thus yielding a larger number of participants. Due to this different approach to
sampling, the present study used data from 2005 and thereafter. In 2005, the CAMS was mailed
to 8,124 individuals and 5,815 responses (3,880 respondents and 1,935 spouses or partners) were
obtained. The same approach was adopted for CAMS 2007 (5,209 responses to 7,741 surveys),
CAMS 2009 (4,954 responses to 7,231 surveys), CAMS 2011(6,531 responses to 9,078 surveys),
and CAMS 2013 (6,000 responses to 8,596 surveys) waves.
Study Sample
The present study used RAND HRS data file version O, a cleaned version of HRS data
with key variables across waves including imputations for income, assets, and cognitive
functioning. Five waves of RAND HRS and CAMS data were merged using respondents’
identification number. Each interview year of HRS (n) and CAMS (n+1) was matched to ensure
that respondents had information for both HRS and CAMS (hereafter Wave 1: CAMS 2005 and
HRS 2004; Wave 2: CAMS 2007 and HRS 2006; Wave 3: CAMS 2009 and HRS 2008; Wave 4:
CAMS 2011 and HRS 2010; and Wave 5: CAMS 2013 and HRS 2012).
26
This study included individuals who had participated in the first wave (CAMS 2005 and
HRS 2004), and they do not need to be present at all five waves. The CAMS was selected as the
master dataset instead of the HRS because the outcome variables of this study are leisure
activities, which come from the CAMS. Of 5,815 respondents from the CAMS 2005, 5,217
individuals were matched with the HRS 2004.
A different exclusion criterion was applied for retirement and widowhood samples. For
the retirement study sample, those who never worked (171 cases), returned to work after
previously retiring (471 cases), or with cognitive impairment (707 cases) were excluded. Based
on the previous literature, individuals who scored below 12 on the cognitive measure were
considered to have cognitive impairment (Crimmins, Kim, Langa, & Weir, 2011). The rationale
for excluding individuals with cognitive impairment is in line with the purpose of this study to
only focus on cognitively healthy older adults. After excluding individuals with missing data for
major variables (266 cases), the final analytic sample was 3,602. For the widowhood sample,
those who were divorced, separated, or never married (897 cases), married after being previously
widowed (52 cases), or with cognitive impairment (664 cases) were excluded. After excluding
individuals with missing data for major variables (274 cases), the final analytic sample was
3,330. Detailed flow charts for both samples are depicted in Figure 2.1 and Figure 2.2,
respectively.
Measures
Dependent variable: Leisure activities. In the CAMS, respondents were asked to
describe how much time they spent on each activity item using a paper-and-pencil module (Hurd
& Rohwedder, 2007). This kind of self-administered survey has a great advantage because it
allows flexible and sufficient time for respondents to recall information, whereas in the presence
27
of an interviewer (e.g., phone or face-to-face interviews), respondents may have limited time to
reflect on their answers (Hurd & Rohwedder, 2007, 2009). The reference period was either the
previous week or previous month. For example, regarding activities assumed to be relatively
frequent (e.g., walking), the number of hours spent during the previous week was sought.
Regarding activities assumed to be less frequent (e.g., volunteering, attending religious services),
the number of hours spent during the previous month was sought. For the present study, 26 of 33
CAMS items were further categorized into four domains of leisure activities: mental, physical,
social, and household activities (see Table 2.1). This classification was largely based on the face
validity and categorization of previous leisure studies (Adams et al., 2011; Lachman et al., 2010;
Paillard-Borg, Wang, Winblad, & Fratiglioni, 2009; H.-X. Wang, Karp, Winblad, & Fratiglioni,
2002). Although considering household activities as leisure may be somewhat controversial,
items such as gardening or caring for pets can be viewed as pleasurable for older adults. Indeed,
several previous studies included household activities as leisure (Chang, Wray, & Lin, 2014;
Paillard-Borg et al., 2009). Seven items were excluded because they did not match any of the
leisure domains (e.g., sleeping and napping, grooming and hygiene, self-managing medical
conditions, taking care of finances or investments), were found to be not beneficial for cognition
(e.g., watching TV), or were mostly engaged in by working individuals (e.g., using computer,
working for pay) and thus were likely to bias the result. For the analyses, monthly based items
were divided by 4 to be comparable to responses regarding weekly based items. Doing one
activity for more than 12 hours a day for 7 days a week (84 hours per week) may not be
common, and thus these responses were considered as possible outliers and recoded as 84 hours
per week. This limit of 12 hour per day (e.g., setting an upper bound to reduce the influence of
outliers) has been adopted by previous studies using the same CAMS data (Fultz, Fisher, &
28
Jenkins, 2004). Each item was summed to indicate weekly hours spent engaged in leisure
activities.
Independent variables.
Retirement status. Although retirement can be defined in various ways (Gustman &
Steinmeier, 2000), this study measured retirement status as withdrawal from the labor force
(Lazear, 1986). Individuals who reported working for pay were considered not retired (coded as
0) and those who reported not working for pay were considered retired (coded as 1). Using
current working status to define participants has been adopted in previous retirement studies
using the same HRS data (Bonsang et al., 2012; Rohwedder & Willis, 2010). Retirement status
was included as a time-varying variable because working individuals could retire during the
study period. As previously mentioned, individuals who never worked or who returned to work
after retirement were excluded in this study.
Widowhood status (widowhood study only). Widowhood status was measured by
respondents’ self-report regarding current marital status. Respondents who were married or
living with a partner were classified as married (coded as 0), as opposed to widowed (coded as
1). Widowhood status was included as a time-varying variable because married individuals could
become widowed during the study period. As previously mentioned, individuals who reported
being separated, divorced, or never married or who became married after being widowed were
excluded in this study.
Time-varying covariates.
Depressive symptoms. Depressive symptoms were measured with a modified 8-item
version of the Center for Epidemiologic Studies Depression Scale.
The measure asked whether
respondents felt (a) depressed, (b) that everything was an effort, (c) their sleep was restless, (d)
29
they could not get going, (e) lonely, (f) they enjoyed life (reverse coded), (g) sad, and (h) happy
(reverse coded) much of the time during the previous week. The range for depressive symptoms
was 0–8 and higher scores indicated more depressive symptoms.
Cognitive function. Cognitive function was measured in three domains; memory (range
= 0–20) based on immediate and delayed word recall, working memory (range = 0–5) based on a
serial 7s test, and processing speed (range = 0–2) based on a backward counting test (Fisher,
Hassan, Rodgers, & Weir, 2013). These domains were combined to compute a total score (0–27),
and higher scores indicated better cognitive functioning. As previously mentioned, individuals
who scored below 12 at baseline were considered cognitively impaired and excluded in this
study.
Self-rated health. Self-perception of health is often a crucial indicator of morbidity and
mortality among older adults (Idler, Kasl, & Lemke, 1990). Thus, this study included self-rated
health as a valid proxy for respondents’ overall health condition. It was measured using one item
with a 5-point scale: “Would you say your health is excellent, very good, good, fair, or poor?”
After reverse coding, higher scores indicated better self-rated health.
Functional limitations. Whether respondents had difficulty with five instrumental
activities of daily living (e.g., shopping for groceries, preparing hot meal, using a phone,
managing money, and taking medication) was measured to indicate functional limitations. These
items (1 = yes, 0 = no) were summed for a total count, but because most responses were zero,
this variable was dichotomized to indicate whether respondents had difficulty with any of the
five items (coded as 1) or no difficulty (coded as 0).
30
Household wealth. Annual household wealth was included as a continuous variable to
indicate respondents’ economic status. Because the distribution was highly skewed, log
transformation (wealth + 1) was applied.
Marital status (retirement study only). Self-report of current marital status was included.
Individuals who reported being married or living with a partner were considered married (coded
as 1), whereas other responses (e.g., separated, divorced, widowed, or never married) were
considered unmarried (coded as 0).
Time-invariant covariates. Age (years), age-squared, gender (1 =male, 0 = female), race
and ethnicity (0 = non-Hispanic White, 1 = non-Hispanic Black, 2 = Hispanic, 3 = other; dummy
coded), and education (years) at baseline were included as time-invariant variables in the present
study.
Analytic Approach
First, descriptive statistics of the study variables were analyzed by each wave. Second,
multilevel modeling was conducted to estimate the impact of retirement and widowhood on
leisure activity engagement from a longitudinal perspective (Singer & Willet, 2003). An
advantage of multilevel modeling is that it does not require individuals to participate in all waves
(Raudenbush & Bryk, 2002). Thus, not every participant was present at all five waves, but they
were all present at the first wave. The multilevel model was composed of two parts: Level 1
described how individuals’ time spent on leisure activities changed over time (within-individual
differences), whereas Level 2 described how these changes varied across individuals (between-
individual differences). Equations for each level in the retirement study were as follows (this was
applied in the widowhood study using a similar approach).
31
𝑌 𝑖𝑗 =
𝜋 0𝑖 + 𝜋 1𝑖 𝑤𝑎𝑣𝑒 𝑖𝑗
+ 𝜋 2𝑖 𝑟𝑒𝑡𝑖𝑟𝑒𝑚𝑒𝑛𝑡 𝑖𝑗
+ 𝜋 3𝑖 𝑤𝑒𝑎𝑙𝑡 ℎ
𝑖𝑗
+ 𝜋 4𝑖 𝑚𝑎𝑟𝑟𝑖𝑒𝑑 𝑖𝑗
+ 𝜋 5𝑖 𝑑𝑒𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝑖𝑗
+ 𝜋 6𝑖 ℎ𝑒𝑎𝑙𝑡 ℎ
𝑖𝑗
+ 𝜋 7𝑖 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑙𝑖𝑚𝑖𝑡𝑎𝑡𝑖𝑜𝑛 𝑖𝑗
+ 𝜋 8𝑖 𝑐𝑜𝑔𝑛𝑖𝑡𝑖𝑜𝑛 𝑖𝑗
+ 𝜀 𝑖𝑗
This equation shows the construct of the Level 1 model (within-individual change) with
time-varying variables. Specifically, 𝑌 𝑖𝑗
denotes leisure activity participation for individual i at
time j; 𝜋 0𝑖 represents individual i’s initial status of leisure time when 𝑤𝑎𝑣𝑒 𝑖𝑗
equals 0 (baseline);
And 𝜋 1𝑖 represents individual i’s rate of change by wave in leisure time. Linear change (𝑤𝑎𝑣𝑒 )
was modeled because when the quadratic term (𝑤𝑎𝑣𝑒 2
) was also included in the model, either
the model fit was no better or the quadratic term was not statistically significant. To reduce the
possible misspecification of linear modeling, baseline age and age-squared variables were
included in the model to clarify the possible curvilinear effect of age. Finally, 𝜋 2𝑖 denotes the
function of retirement on leisure time and 𝜋 3𝑖 – 𝜋 8𝑖 can be interpreted likewise; 𝜀 𝑖𝑗
indicates
Level 1 residuals, which describes the deviation of individual i at time j from the overall
intercept and slope (Singer & Willet, 2003).
𝜋 0𝑖 = 𝛾 00
+ 𝛾 01
𝑎𝑔𝑒 𝑖 + 𝛾 02
𝑎𝑔𝑒 2
𝑖 + 𝛾 03
𝑚𝑎𝑙𝑒 𝑖 + 𝛾 04
𝑏𝑙𝑎𝑐𝑘 𝑖 + 𝛾 05
ℎ𝑖𝑠𝑝𝑎𝑛𝑖𝑐𝑠 𝑖 + 𝛾 06
𝑜𝑡 ℎ𝑒𝑟
𝑖 + 𝛾 07
𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 𝑖 + 𝜁 0𝑖
This equation shows how 𝜋 0𝑖 (initial level of leisure time) can vary between individuals as
influenced by baseline age (𝛾 01
), age-squared (𝛾 02
), gender (𝛾 03
), race and ethnicity
(𝛾 04
, 𝛾 05
, 𝛾 06
), and years of education (𝛾 07
). Specifically, 𝛾 00
represents the average initial level
(𝜋 0𝑖 ) for individuals with all other predictors equal to zero; 𝜁 0𝑖 denotes residual variance, or
person i’s deviation from the overall sample’s intercept. The following equations show the
constructs of the Level 2 model (between-individual differences in change).
𝜋 1𝑖 = 𝛾 10
+ 𝜁 1𝑖
32
𝜋 2𝑖 = 𝛾 20
𝜋 3𝑖 = 𝛾 30
𝜋 4𝑖 = 𝛾 40
𝜋 5𝑖 = 𝛾 50
𝜋 6𝑖 = 𝛾 60
𝜋 7𝑖 = 𝛾 70
𝜋 8𝑖 = 𝛾 80
In the first equation, 𝛾 10
is a parameter for the slope of the wave (𝜋 1𝑖 ) with residual
variance of 𝜁 1𝑖 . Likewise, 𝛾 20
– 𝛾 80
in the subsequent equations are the parameters for retirement,
household wealth, marital status, depressive symptom, self-rated health, functional limitation,
and cognitive function, respectively. Residual variance for these parameters were not assigned
because estimating random slopes for all Level 1 coefficients may not be parsimonious
(McCoach & Kaniskan, 2010; Raudenbush & Bryk, 2002).
All analyses were conducted separately for retirement and widowhood study using the
XTMIXED command in Stata (version 12.0). Missing observations for outcome variables were
handled with maximum likelihood estimation.
Results
Characteristics of Retirement Study Sample
Table 2.2 shows the means and standard deviations of the major study variables in the
retirement sample by wave. The sample size for Wave 1 (n = 3,602) is same as the final analytic
sample because all individuals were present at the first wave. Thus, the first column of Table 2.2
shows the baseline characteristics for all study variables and the remaining four columns show
the characteristics of time-varying variables by wave. At baseline, the mean age of the sample
33
was 66.48 (SD = 9.41); 42% of participants were male and this sample had 13.17 years of
education on average (SD = 2.65). A majority of the sample was non-Hispanic White (85%),
retired (57%), and married (70%). Average household wealth was $480,110 (SD = $962.97). In
terms of health factors, mean scores were 1.15 (SD = 1.73) for depressive symptoms, 16.98 (SD
= 2.95) for cognitive function, and 2.35 (SD = 1.07) for self-rated health. About 8% of the
sample reported having some degree of functional limitations. Regarding leisure variables, the
average time spent on mental activities was 22.30 hours per week (SD = 17.91), followed by 8.36
hours (SD = 11.75) for physical activities, 21.00 hours (SD = 18.47) for social activities, and
23.30 hours (SD = 18.65) for household activities. Over time, the proportion of individuals who
had retired increased from 57% (Wave 1) to 76% (Wave 5).
Multilevel Analysis: Retirement and Leisure Activity Engagement
The results of mixed-effects modeling of retirement and leisure activity engagement
while controlling for age, age-squared, gender, education, race and ethnicity, marital status,
household wealth, depressive symptoms, self-rated health, cognitive function, and functional
limitations are presented in Table 2.3. In the fixed-effects section, both time-invariant and time-
varying covariates are presented with unstandardized coefficients and standard errors. In the
random-effects section, unpredicted residuals within individuals (Level 1) and unexplained
variance of individuals in both initial status and rate of change (Level 2) are indicated. The
significance of random effects indicates that significant within- and between-individual variances
remain to be explained, even after controlling for all variables in this model. The interpretation
only focuses on the fixed effects. Because time spent engaged in leisure activities was skewed, a
model with log transformation of leisure activities was also conducted and results are presented
in Appendix 2.A. The significance and sign of the parameters for retirement, intercept, and rate
34
of change for both models (before transformation: Table 2.3; after transformation: Appendix
2.A) were almost identical, so interpretation is based on the pretransformation model for more
meaningful interpretation with the original unit of time.
As presented in Table 2.3, intercepts indicate the average amount of time spent per week
in the sample in each domain of leisure activities at baseline. The average hours per week
engaged in mental activities was 16.19, followed by 7.58 for physical, 15.58 for social, and 28.87
for household activities. The rate of change over time was significantly negative for all four
domains of activities (mental: b = -0.29, SE = 0.10, p < .01; physical: b = -0.16, SE = 0.06, p <
.05; social: b = -0.34, SE = 0.10, p < .01; household: b = -0.61, SE = 0.10, p < .001), which
indicates that time spent engaged in leisure activities decreased during this 8-year period. For
example, between each wave, engagement in mental activities decreased an average of 0.29
hours per week.
The statistically significant coefficients of the retirement variable in terms of mental (b =
1.70, SE = 0.43, p < .001), social (b = 2.94, SE = 0.45, p < .001), and household (b = 4.21, SE =
0.44, p < .001) activities indicate that individual changes in leisure engagement during the 8-year
period were positively influenced by retirement status. For example, transitioning from working
to retirement status was associated with increased engagement in mental activities by an average
of 1.7 hours per week. Similarly, the transition to retirement from working status was related to
increased engagement in social activities by an average of 2.94 hours per week and household
activities by 4.21 hours per week.
Characteristics of Widowhood Sample
Table 2.4 presents the means and standard deviations of the major study variables in the
widowhood sample by wave. At baseline, 3,330 respondents were present. The mean age of the
35
sample was 66.85 (SD = 9.30); participants had an average of 13.16 (SD = 2.66) years of
education. A majority of the sample was female (58%), non-Hispanic White (88%), retired
(59%), and married (82%). Average household wealth was $519,140 (SD = $995.05). With
regard to health, mean scores were 1.04 (SD = 1.64) for depressive symptoms, 17.01 (SD = 2.98)
for cognitive function, and 2.40 (SD = 1.04) for self-rated health. Only about 7% of the sample
reported having any difficulty with instrumental activities of daily living. Regarding leisure
variables, the average weekly time spent was 22.42 hours (SD = 17.48) for mental, 8.32 hours
(SD = 11.68) for physical, 21.37 hours (SD = 18.81) for social, and 23.61 (SD = 18.80) for
household activities. The proportion of widowed individuals increased from 18% (Wave 1) to
25% (Wave 5) over time.
Multilevel Analysis: Widowhood and Leisure Activity Engagement
Table 2.5 presents the result of multilevel modeling estimating the impact of widowhood
on leisure activity engagement after controlling for age, age-squared, gender, education, race and
ethnicity, retirement status, household wealth, depressive symptoms, self-rated health, cognitive
function, and functional limitations. Like the retirement study, a model with log transformation
of leisure activities was also conducted and results are presented in Appendix 2.B. The
significance and sign of the parameters for widowhood, intercept, and rate of change for before
transformation (Table 2.5) and after transformation (Appendix 2.B) were almost identical, so
interpretation is based on the pretransformation model for more meaningful interpretation with
the original unit of time.
Table 2.5 shows the initial level of average hours per week spent on mental (b = 15.33,
SE = 1.56), physical (b = 7.27, SE = 0.94), social (b = 15.54, SE = 1.64), and household (b =
32.54, SE = 1.67) activities. The negative coefficients for change rates indicate that these
36
individuals decreased their engagement in leisure activities during the 8-year period in all four
domains (mental: b = -0.26, SE = 0.10, p < .01; physical: b = -0.18, SE = 0.06, p < .01; social: b
= -0.27, SE = 0.11, p < .05; household: b = -0.60, SE = 0.11, p < .01). Last, only household
activities (b = -2.96, SE = 0.59, p < .001) were significantly influenced by widowhood status. In
other words, transitioning from married to widowed was associated with decreased engagement
in household activities by an average of 2.96 hours per week. Other leisure domains (mental,
physical, and social activities) were not significantly influenced by widowhood.
Discussion
This study examined how leisure participation changed and was influenced by retirement
or widowhood during an 8-year period among adults aged 51 or older in the United States.
Engagement in mental, physical, social, and household activities was analyzed with multilevel
modeling using five waves of national panel data from the HRS and its supplementary CAMS
(2005–2013). Due to different sample selection criteria, retirement and widowhood studies had
to be conducted separately. A sample of 3,602 older adults was included in the retirement study,
whereas 3,330 older adults were included in the widowhood study.
The study findings indicate that individuals’ time spent on mental, physical, social, and
household leisure activities significantly decreased during the 8-year period (addressing
Research Question 2.1) after controlling for all other covariates including age. Age (at baseline)
was also negatively related with time spent on physical, social, and household leisure activities,
which is consistent with previous longitudinal studies showing that individuals are less likely to
engage in activities as they age (Armstrong & Morgan, 1998; Strain et al., 2002). On the other
hand, older age was positively related with more time spent engaging in mental activities. The
mental activities domain includes items such as reading books and newspapers, which are largely
37
in-home based and physically nondemanding; older individuals may tend to engage more in
these activities as they become older (Iso-Ahola, Jackson, & Dunn, 1994).
Impact of Retirement on Leisure Activity Engagement
Regarding the second research question (i.e., how retirement influences individuals’
leisure activity engagement), findings show that transitioning from working to retired status was
associated with an increase in time spent on mental, social, and household activities. This
positive association is noteworthy because the overall trend of these activities was significantly
decreasing over time. Indeed, retired individuals may have replaced their time previously
dedicated to work with compensatory activities, thus increasing their leisure pursuits. It is also
consistent with previous studies that found involvement in reading (Rosenkoetter et al., 2001),
informal social activities (e.g., getting together with friends; Janke et al., 2006), and household
activities (Szinovacz, 2000) increased with retirement.
On the other hand, time spent on physical activities was not significantly influenced by
retirement. This is contrary to previous longitudinal studies showing that physical activity
increased after retirement (Evenson et al., 2002; Janke et al., 2006). However, this may due to
the use of different items to measure physical activities or classifying physical activities into
subtypes (Berger et al., 2005; Evenson et al., 2002; Slingerland et al., 2007). For example,
Evenson et al. (2002) included work activities (e.g., frequency of walking while at work) as one
part of physical activities. Berger et al. (2005) classified physical activities as activities done at
work, at home, or during leisure time and found that physical activity at work (e.g., physically
demanding activities, the number of stairs climbed daily) dramatically decreased after retirement,
whereas activities at home or during leisure remained more or less constant. In this respect,
potentially increased engagement in physical activities after retirement may have been offset by
38
decreased engagement in physical activities at work in this study. However, because physical
activity items in the CAMS questionnaire did not further inquire as to the purpose of the activity
(e.g., walking for work or leisure), it is difficult to delineate how time spent at work or for leisure
changed or were influenced by retirement. Thus, future studies specifically asking about the
context of leisure activity participation will provide a better understanding of retirement’s impact
on leisure activity participation, especially for physical activities.
Impact of Widowhood on Leisure Activity Engagement
The third research question asked how widowhood influences individuals’ leisure activity
engagement. As presented in Table 2.5, the transition from married to widowhood status was
associated with significantly decreased time involved in household activities (e.g., cleaning,
home improvements, yard working, maintaining vehicles, caring for pets), which is only partly
consistent with previous findings. For example, Strain et al. (2002) found that widowhood was
significantly associated with ceasing outdoor yard work. On the other hand, Utz et al. (2002)
showed widowhood was associated with increased household activities for men and similar
levels of engagement for women.
The study findings indicate that the amount of household work may have decreased
following the death of a spouse. Lack of a partner at home may have led to decreased motivation
to engage in household activities among widowed individuals. However, this may be also
affected by cohabiting individuals in the household postwidowhood (e.g., living alone vs. with
other family members). For example, South and Spitze (1994) found that cohabiting with an
adult son increased household work among women, whereas cohabiting with an adult daughter
reduced household work for both women and men. Moreover, increased intergenerational
support following widowhood as a filial norm (e.g., adult children helping with daily household
39
chores; Silverstein, Gans, & Yang, 2006) may have decreased the participation of widowed
individuals in household activities. Thus, future studies will be needed to examine living
arrangements and intergenerational support among widowed individuals as predictors of
engagement in household activities. Examining this research question from a gender perspective
is further recommended for future studies.
Findings show that mental, physical, and social activities were not significantly
influenced by widowhood. This is inconsistent with previous studies. For example, Wilcox et al.
(2003) found that long-term widowed individuals increased their engagement in physical
activities when compared to those who remained married in a 3-year prospective study.
Likewise, Utz et al.’s (2002) study showed that informal social activities (e.g., getting together or
talking on the phone with friends, neighbors, or relatives) increased, whereas formal social
activities (e.g., volunteering, attending meetings, religious services) remained consistent
following widowhood. This discrepancy may be due to previous studies using a women-only
sample (Wilcox et al., 2003) or measuring activities based on frequency (Utz et al., 2002) instead
of duration.
Several limitations of the present study should be acknowledged. First, certain items in
this study were defined as leisure activities, although individuals might not have engaged in
these activities by choice. For example, participation in some of the items in the household
activities (e.g., house cleaning, washing clothes) or social activities (e.g., helping friends,
neighbors, or relatives who do not live with respondents and did not pay for the help) domains
may be out of necessity rather than enjoyment. In a qualitative leisure study, Gibson et al. (2003)
contended that the “central feature [of leisure] was the ability to choose what they wanted to do”
(p. 221). In this respect, future studies including additional information on individual freedom of
40
choice to engage in certain activities will enrich the interpretation of findings. Second, although
the classification of four domains of activities was largely derived from previous relevant
studies, these domains may not necessarily be mutually exclusive. For example, the shopping
item was classified as a household activity but can also serve as a mental, physical, or social
activity in certain contexts. To date, there is a lack of consensus regarding a valid scale to
measure or classify leisure activities, which makes it hard to compare results from one study to
another. Thus, more valid measurement of leisure activity domains needs to be established in this
field of study. Last, the focus of this study was on the transition itself, but future studies should
delineate what happens during the years before and after retirement or widowhood to provide a
better understanding of leisure engagement trajectories.
Conclusion
This study’s findings indicate that transition to retirement is related to increased time
spent in mental, social, household activities, whereas widowhood is related to decreased time
spent in household activities. This suggests further exploration is needed of these significant
changes in each domain of leisure participation and their influences on various health outcomes.
Encouraging leisure pursuits among individuals who experience either or both life transitions
(retirement and widowhood) might help them maintain better health in later life; thus, future
studies examining the mechanisms among life transitions, leisure activities, and health outcomes
are encouraged in this field of research.
41
Table 2.1. Subdomains of Leisure Activities from the CAMS
Mental (7 Items) Physical (2 Items) Social (9 Items) Household (8 Items)
Reading newspapers or
magazines
Reading books
Playing cards or games, or
solving puzzles
Doing arts and craft projects,
including knitting, embroidery,
or painting
Listening to music
Singing or playing a musical
instrument
Praying or meditating
Walking
Participating in sports or other
exercise activities
Visiting in person with friends,
neighbors, or relatives
Communicating by telephone,
letters, or email with friends,
neighbors, or relatives
Helping friends, neighbors, or
relatives
Physically showing affection
for others through hugging,
kissing, etc.
Doing volunteer work for
religious, educational, health-
related, or other charitable
organizations
House cleaning
Preparing meals and cleaning
up afterward
Washing, ironing, or mending
clothes
Shopping or running errands
Home improvements,
including painting,
redecorating, or making home
repairs
Yard work or gardening
Working on, maintaining, or
cleaning a car or vehicle
Caring for pets
42
Attending religious services
Attending meetings of clubs or
religious groups
Attending concerts, movies, or
lectures, or visiting museums
Dining or eating outside the
home (not related to business
or work)
Note: CAMS, Consumption and Activities Mail Survey; 7 items (i.e., “watching television,” “sleeping and napping,” “grooming and hygiene,” “using computer,”
“working for pay,” “taking care of finances or investments, such as banking, paying bills, balancing the checkbook, doing taxes,” and “self-treating or self-
managing an existing medical condition”) were excluded from this study.
43
Table 2.2. Characteristics of the Retirement Study Sample by Wave
Wave 1 Wave 2 Wave 3 Wave 4 Wave 5
(n = 3,602) (n = 3,133) (n = 2,923) (n = 2,669) (n = 2,385)
% or M (SD) % or M (SD) % or M (SD) % or M (SD) % or M (SD)
Time-invariant variables
Age (range = 51–98) 66.48 (9.41)
Male .42
Non-Hispanic Black .08
Hispanic .05
Other race and ethnicity .02
Education (range = 0–17) 13.17 (2.65)
Time-variant variables
Retired (vs. working) .57 .62 .67 .72 .76
Married (vs. unmarried) .70 .69 .68 .66 .65
Household wealth ($1,000) 480.11 (962.97) 583.45 (1,279) 578.20 (1,048.28) 546.48 (969.25) 546.29 (964.13)
Depressive symptoms (range = 0–8) 1.15 (1.73) 1.18 (1.75) 1.17 (1.77) 1.14 (1.75) 1.14 (1.79)
44
Cognitive function (range = 0–27) 16.98 (2.95) 16.50 (3.62) 16.29 (3.67) 15.83 (3.82) 15.65 (3.83)
Self-rated health (range = 0–4) 2.35 (1.07) 2.36 (1.04) 2.27 (1.03) 2.30 (1.02) 2.29 (1.02)
Functional limitations (vs. none) .08 .08 .09 .10 .11
Outcome variables (hours per week)
Mental activities 22.30 (17.91) 22.66 (18.10) 21.96 (17.71) 21.61 (17.75) 21.87 (18.93)
Physical activities 8.36 (11.75) 8.47 (12.16) 8.26 (11.73) 7.88 (11.10) 7.83 (10.38)
Social activities 21.00 (18.47) 21.15 (19.11) 20.49 (17.95) 20.34 (18.43) 21.10 (19.30)
Household activities 23.30 (18.65) 22.61 (17.93) 22.79 (18.22) 22.37 (18.44) 22.01 (19.24)
45
Table 2.3. Multilevel Model of Retirement and Leisure Activity Engagement
Mental Physical Social Household
b SE b SE b SE b SE
Fixed effects
Intercept 16.19*** 1.54 7.58*** 0.94 15.58*** 1.51 28.87*** 1.53
Change rate -0.29** 0.10 -0.16* 0.06 -0.34** 0.10 -0.61*** 0.10
Retirement 1.70*** 0.43 -0.49 0.30 2.94*** 0.45 4.21*** 0.44
Covariates
Age 0.17*** 0.03 -0.08*** 0.02 -0.12*** 0.03 -0.22*** 0.03
Age
2
0.001 0.002 0.001 0.001 -0.010* 0.002 -0.010*** 0.002
Male -4.66*** 0.49 1.66*** 0.28 -5.68*** 0.46 -7.84*** 0.48
Education 0.54*** 0.10 -0.07 0.06 0.24** 0.09 -0.47*** 0.10
Black 3.56*** 0.90 0.48 0.52 1.50 0.84 -1.98* 0.89
Hispanic 0.23 1.10 1.03 0.63 -1.04 1.03 2.62* 1.08
Other race and ethnicity 2.14 1.85 1.82 1.07 2.24 1.73 -0.54 1.82
Married -0.24 0.43 -0.31 0.27 -0.18 0.44 2.10*** 0.43
46
Household wealth -0.20* 0.09 0.11 0.06 0.12 0.10 0.01 0.09
Depressive symptoms -0.08 0.09 -0.03 0.06 -0.12 0.10 -0.05 0.09
Self-rated health 0.18 0.17 0.77*** 0.11 0.84*** 0.18 0.49** 0.17
Cognitive function 0.04 0.05 -0.04 0.03 0.06 0.05 -0.01 0.05
Functional limitations -0.46 0.53 -0.91* 0.36 -0.39 0.58 -3.32*** 0.53
Random effects
Within individual 155.64* 2.45 86.47* 1.34 215.66* 3.34 161.41* 2.54
Intercept 160.43* 6.45 51.76* 2.67 122.12* 6.55 159.81* 6.57
Change rate 6.55* 0.69 1.06* 0.30 4.46* 0.81 7.02* 0.73
*p < .05. **p < .01. ***p < .001.
47
Table 2.4. Characteristics of the Widowhood Study Sample by Wave
Wave 1 Wave 2 Wave 3 Wave 4 Wave 5
(n = 3,330) (n = 2,911) (n = 2,719) (n = 2,470) (n = 2,245)
% or M (SD) % or M (SD) % or M (SD) % or M (SD) % or M (SD)
Time-invariant variables
Age (range = 51–98) 66.85 (9.30)
Male .42
Non-Hispanic Black .06
Hispanic .05
Other race and ethnicity .01
Education (range = 0–17) 13.16 (2.66)
Time-variant variables
Widowed (vs. married) .18 .20 .21 .23 .25
Retired (vs. working) .59 .64 .66 .71 .74
Household wealth ($1,000) 519.14 (995.05) 624.83 (1,316.01) 621.32 (1,091.67) 589.26 (1,006.61) 578.67 (988.67)
Depressive symptoms (range = 0–8) 1.04 (1.64) 1.08 (1.65) 1.07 (1.66) 1.05 (1.67) 1.05 (1.68)
48
Cognitive function (range = 0–27) 17.01 (2.98) 16.49 (3.61) 16.25 (3.69) 15.87 (3.83) 15.70 (3.83)
Self-rated health (range = 0–4) 2.40 (1.04) 2.41 (1.01) 2.33 (1.00) 2.37 (0.99) 2.34 (0.99)
Functional limitations (vs. none) .07 .08 .08 .09 .10
Outcome variables (hours per week)
Mental activities 22.42 (17.48) 22.63 (17.73) 22.16 (17.73) 21.98 (17.45) 21.90 (18.16)
Physical activities 8.32 (11.68) 8.43 (11.47) 8.21 (11.11) 7.97 (11.04) 7.79 (10.45)
Social activities 21.37 (18.81) 21.25 (18.40) 20.73 (17.82) 20.63 (18.25) 21.18 (18.39)
Household activities 23.61 (18.80) 23.06 (18.32) 22.82 (17.97) 22.74 (18.68) 22.00 (18.86)
49
Table 2.5. Multilevel Model of Widowhood and Leisure Activity Engagement
Mental Physical Social Household
b SE b SE b SE b SE
Fixed effects
Intercept 15.33*** 1.56 7.27*** 0.94 15.54*** 1.64 32.54*** 1.67
Change rate -0.26** 0.10 -0.18** 0.06 -0.27* 0.11 -0.60*** 0.11
Widowhood 0.91 0.55 0.38 0.34 -0.37 0.58 -2.96*** 0.59
Covariates
Age 0.17*** 0.03 -0.09*** 0.02 -0.11*** 0.03 -0.23*** 0.03
Age
2
-0.002 0.002 .0002 0.001 -0.010* 0.002 -0.010** 0.002
Male -5.17*** 0.50 1.75*** 0.29 -5.59*** 0.49 -8.41*** 0.51
Education 0.56*** 0.10 -0.04 0.06 0.15 0.10 -0.48*** 0.10
Black 3.39** 0.99 -0.13 0.57 1.02 0.96 2.49* 1.01
Hispanic 1.15 1.13 0.83 0.65 -0.61 1.10 3.39** 1.15
Other race and ethnicity 4.07* 2.02 3.00* 1.16 5.62** 1.95 2.50 2.05
Retirement 1.40*** 0.40 -0.44 0.26 2.84*** 0.47 3.99*** 0.46
50
Household wealth -0.04 0.10 0.14* 0.07 -0.01 0.12 -0.02 0.11
Depressive symptoms -0.17 0.10 -0.09 0.07 -0.18 0.12 -0.08 0.11
Self-rated health 0.29 0.18 0.75*** 0.12 0.83*** 0.21 0.33 0.21
Cognitive function 0.03 0.05 -0.08* 0.03 0.19** 0.06 -0.04 0.05
Functional limitations 0.42 0.55 -1.10** 0.38 -0.93 0.68 -4.38*** 0.65
Random effects
Within individual 154.19* 2.51 82.10* 1.31 212.14* 4.32 172.14* 3.55
Intercept 150.43* 6.41 49.18* 2.64 121.94* 8.34 146.37* 7.93
Change rate 5.32* 0.67 1.46* 0.31 6.01* 1.00 6.74* 0.90
*p < .05. **p < .01. ***p < .001.
51
Figure 2.1. Sample Selection Criteria for Retirement Study
CAMS 2005–2013
N = 5,815
n = 5,217
n = 5,046
n = 4,575
Excluded those who went back to
work after previously retiring
during the study period: n = 471
Excluded individuals with
cognitive impairment (score <12)
at baseline: n = 707
n = 3,868
n = 3,602
Excluded missing cases in the
study variables: n = 266
Individually matched with HRS
data; excluded those younger than
51 at baseline: n = 598
Excluded those who never
worked: n = 171
52
Figure 2.2. Sample Selection Criteria for Widowhood Study
CAMS 2005–2013
N = 5,815
n = 5,217
n = 4,320
n = 4,268
Excluded those who married after
being widowed during the study
period: n = 52
Excluded individuals with
cognitive impairment (score < 12)
at baseline: n = 664
n = 3,604
n = 3,330
Excluded missing cases in the
study variables: n = 274
Individually matched with HRS
data; excluded those younger than
51 at baseline: n = 598
Excluded those who were
divorced, separated, or never
married: n = 897
53
Appendix 2.A
Log-Transformed Multilevel Model of Retirement and Leisure Activity Engagement
Mental Physical Social Household
b SE b SE b SE b SE
Fixed effects
Intercept 1.03*** 0.03 0.50*** 0.04 1.07*** 0.03 1.35*** 0.03
Change rate -0.01*** 0.002 -0.01** 0.002 -0.01*** 0.002 -0.02*** 0.002
Retirement 0.04*** 0.01 0.01 0.01 0.05*** 0.01 0.07*** 0.01
Covariates
Age 0.004*** 0.001 -0.004*** 0.001 -0.002** 0.001 -0.01*** 0.001
Age
2
-0.0001 0.00004 -0.00004 0.0001 -0.0002*** 0.00004 -0.0003*** 0.00005
Male -0.10*** 0.01 0.08*** 0.01 -0.13*** 0.009 -0.17*** 0.01
Education 0.01*** 0.002 0.01** 0.002 0.01*** 0.002 -0.01*** 0.002
Black 0.04* 0.02 0.01 0.02 0.01 0.002 -0.05** 0.02
Hispanic -0.003 0.02 0.08** 0.03 -0.05* 0.02 0.01 0.02
Other race and ethnicity -0.001 0.03 0.05 0.04 -0.04 0.03 -0.01 0.04
54
Married -0.002 0.01 -0.02 0.01 -0.003 0.01 0.02** 0.01
Household wealth -0.001 0.002 0.01** 0.002 0.004* 0.002 0.005* 0.002
Depressive symptoms -0.004* 0.002 -0.004 0.002 -0.01** 0.002 -0.004* 0.002
Self-rated health 0.01** 0.003 0.04*** 0.004 0.02*** 0.003 0.02*** 0.003
Cognitive function 0.003** 0.001 0.001 0.001 0.002* 0.001 0.003** 0.001
Functional limitations -0.01 0.01 -0.07*** 0.01 -0.02 0.01 -0.14*** 0.01
Random effects
Within individual 0.05* 0.001 0.10* 0.002 0.06* 0.001 0.06* 0.001
Intercept 0.06* 0.002 0.09* 0.004 0.05* 0.002 0.06* 0.002
Change rate 0.002* 0.0002 0.09* 0.004 0.002* 0.0002 0.005* 0.003
*p < .05. **p < .01. ***p < .001.
55
Appendix 2.B
Log-Transformed Multilevel Model of Widowhood and Leisure Activity Engagement
Mental Physical Social Household
b SE b SE b SE b SE
Fixed effects
Intercept 1.04*** 0.03 0.49*** 0.04 1.08*** 0.03 1.42*** 0.03
Change rate -0.01*** 0.002 -0.01* 0.002 -0.01*** 0.002 -0.02*** 0.002
Widowhood 0.02 0.01 0.02 0.01 0.003 0.01 -0.04*** 0.01
Covariates
Age 0.004*** 0.001 -0.004*** 0.001 -0.002** 0.001 -0.01*** 0.001
Age
2
-0.0001** 0.00005 -0.0001 0.0001 -0.0002*** 0.00005 -0.0003*** 0.00005
Male -0.11*** 0.01 0.08*** 0.01 0.12*** 0.01 -0.18*** 0.01
Education 0.01*** 0.002 0.01** 0.002 0.01** 0.002 -0.01*** 0.002
Black 0.04** 0.02 -0.01 0.02 0.02 0.02 -0.05* 0.02
Hispanic 0.003 0.02 0.07* 0.03 -0.04 0.02 0.02 0.02
Other race and ethnicity 0.02 0.04 0.06 0.05 0.02 0.04 0.07 0.04
56
Retirement 0.03*** 0.001 0.01 0.01 0.05*** 0.01 0.07*** 0.01
Household wealth 0.002 0.002 0.01** 0.003 0.001 0.002 0.004 0.002
Depressive symptoms -0.005** 0.002 -0.01* 0.003 -0.01** 0.002 -0.01* 0.002
Self-rated health 0.01*** 0.003 0.04*** 0.005 0.02*** 0.004 0.01** 0.004
Cognitive function 0.003** 0.0001 0.001 0.001 0.004*** 0.001 0.003 0.001
Functional limitations -0.003 0.01 -0.08*** 0.02 -0.03** 0.01 -0.17**** 0.01
Random effects
Within individual 0.05* 0.001 0.10* 0.002 0.06* 0.001 0.06* 0.001
Intercept 0.05* 0.002 0.08* 0.005 0.05* 0.003 0.06* 0.003
Change rate 0.002* 0.0002 0.002* 0.0005 0.002* 0.0003 0.004* 0.0004
*p < .05. **p < .01. ***p < .001.
57
Chapter 3: Retirement and Cognition among Older Adults: The Role of Leisure Activities
Introduction
Maintaining cognitive functioning in later life is one of the essential aspects of successful
aging (Rowe & Kahn, 1997) and it is associated with better psychological well-being (Llewellyn
et al., 2008) and retention of autonomy among older adults. Accordingly, there has been a
significant effort to identify predictors of retaining cognition. Based on cognitive reserve theory,
educational attainment, occupational complexity, and later-life leisure engagement are
considered to expand cognitive reserves, thus providing more resilience to brain damage or
neurodegenerative disorders like Alzheimer’s disease (Scarmeas & Stern, 2003; Y. Stern, 2002).
Indeed, previous empirical studies have shown that individuals with more years of education
(Gatz et al., 2001; Y. Stern et al., 1994), with a complex occupation (Andel, Silverstein, et al.,
2015), and who participate more frequently in leisure activities (Podewils et al., 2005; Verghese
et al., 2003; J. Y. J. Wang et al., 2006) show less cognitive decline or lower risk of Alzheimer’s
disease. Among these protective factors, however, engagement in leisure activities (e.g., mental,
social activities), which is still modifiable in later life, has received increasing attention during
the last two decades.
Retirement is one of the most salient life transitions in later adult life (M. Wang,
Henkens, & van Solinge, 2011) because it largely affects older individuals’ engaged lifestyles,
including allocation of time and energy once dedicated to work. The “use or lose it” perspective
contends that use of cognitive skills by engaging in activities may help maintain cognitive
performance, thereby protecting against cognitive declines in later life (Hultsch et al., 1999).
However, retirees may use relatively fewer cognitive skills than workers because workplaces
more often provide a cognitively challenging environment than nonwork settings (Rohwedder &
58
Willis, 2010). In this respect, retirement may become a significant risk factor for cognitive
decline by eliminating mentally stimulating routines and environments previously provided in
the workplace unless sufficiently compensated by leisure activities (e.g., doing crossword
puzzles) to offset such loss (Rohwedder & Willis, 2010). Accordingly, leisure activity
engagement may become a crucial part of adjustment for retirees (Rosenkoetter et al., 2001) to
maintain cognitive function postretirement.
Another life of inquiry noted that retirement frees individuals from work responsibilities
but also deprives them of the structure and sense of belonging once provided by work
(Rosenkoetter et al., 2001). Indeed, some previous studies have shown that retirement has an
adverse effect on both physical and mental health outcomes such as cardiovascular disease,
functional limitations, and depressive symptoms (Dave et al., 2006; Moon et al., 2012). Kim and
Moen (2002) mentioned that “the retirement passage itself may lead to diminished well-being, as
individuals lose their occupational attachments, their social network of coworkers, and a major
anchor for their identities” (p. 212). Pearlin (1989) also argued that retirement could be liberating
if considered a new opportunity for pursuing passions other than work, but also depriving if
perceived as the loss of a previous status or skills. Thus, interruption in the sense of self
(Bridges, 2004) and the loss of an important role (Pinquart & Schindler, 2007) postretirement
may also influence older adults’ health, including cognitive function.
Nevertheless, few studies have examined the specific impact of retirement on cognitive
function and have generated inconsistent findings. For example, Bonsang et al. (2012) showed a
significant negative impact of retirement on cognition among older Americans. Similarly,
Rohwedder and Willis (2010) conducted a cross-national study (i.e., United States, England, and
Europe) and found negative effects of early retirement on cognitive ability. In contrast, Roberts
59
et al. (2011) found no significant relationship between time spent retired and cognition in a
London-based sample. Likewise, Coe et al. (2012) and Coe and Zamarro (2011) found no causal
relationship between retirement and cognition in a men-only U.S. sample, evidenced by a
significant effect disappearing after controlling for endogeneity retirement factors (e.g.,
retirement age, early retirement offer). The most recent relevant study by Adam et al. (2013)
found that even after controlling for endogeneity of retirement decisions, retirement still had a
significant negative impact on cognitive functioning. Such equivocal findings regarding the
retirement–cognition association suggests further exploration is needed to test whether other
factors affect this relationship.
Leisure activities have often been considered as beneficial strategies or resources for
retirement transition and adjustment (M. Wang & Schultz, 2010) and as a positive value to link
an individual’s identity between pre- and postretirement life (Atchley, 1971). Nevertheless, few
studies have delineated the role of leisure activities with a specific focus on the retirement–
cognition relationship. Few relevant studies have tested whether leisure activities play a
moderating role in life events and health outcomes, including functional limitation (Unger et al.,
1997) and quality of life (Silverstein & Parker, 2002), and have not investigated its effect
specifically on cognition. Also, the few existing studies on cognitive function have largely tested
moderating role of leisure activity in terms of genetic (e.g., ApoE4 allele), early life (e.g.,
education), or midlife (e.g., occupational complexity) factors (Andel, Silverstein, et al., 2015;
Lachman et al., 2010; Lee & Chi, 2016; Niti et al., 2008), but not on later-life transitions such as
retirement.
Up to my knowledge, only one identified study (Andel, Finkel, & Pedersen, 2015)
examined the interacting effects of work complexity and leisure activity on cognitive aging
60
before and after retirement, specifically among Swedish older adults. Findings imply that
engagement in leisure activities after retirement may compensate for cognitive disadvantage
among individuals with lower work complexity. However, the authors mentioned the study’s
relatively small convenience sample as a limitation (n = 421, a subsample of a Swedish twin
study) and that a larger sample was needed to validate their findings. Moreover, although
cognitive function before and after retirement was measured as the outcome, the major focus of
this study was work complexity rather than retirement per se, leaving the mechanism among
retirement, leisure activities, and cognition unexplored. Adam et al. (2013) noted that retirement
may imply changes in activities, which may in turn contribute to cognitive function among older
adults. These authors further suggested future studies that examine “whether the relationship
between retirement and cognition is direct and/or whether there are some intermediate variables
between retirement and cognition” (p. 388). In this vein, testing a mediation model of leisure
activities in the relationship between retirement and cognitive function seems to be the next step
in this field of research.
Thus, this study examined the effect of retirement on cognition functioning and further
delineated the mediating role of leisure participation in the relationship between retirement and
cognition using a national longitudinal survey, the HRS, merged with its supplementary data, the
CAMS. Because transitioning from work to retirement can be a crucial period for individuals to
change their participation in leisure activities, this study classified retirement status into three
groups: remained working, transitioned to retirement, and remained retired. The last two
retirement groups were compared with the first group with regard to cognitive function during a
4-year period (using three waves of the HRS and CAMS). The rationale for using only three
waves of HRS is derived from the findings of a previous relevant study (Bonsang et al., 2012)
61
indicating that the most salient negative effect of retirement on cognitive functioning is likely to
occur approximately 1 year postretirement.
Due to the scarcity of longitudinal data repeatedly measuring leisure activities among the
same individuals, most leisure studies have not controlled for baseline leisure activities unless
specifically measuring change. Thanks to CAMS data, which longitudinally measured
individuals’ activity participation over time, this study controlled for previous levels of leisure
activity participation in addition to examining leisure as a mediator in the model. The aim of this
study is to understand how to improve cognitive function in postretirement life for older adults
who experience retirement. If leisure engagement is a significant coping resource that protects
against cognitive decline for this population, cognitive adjustment during postretirement life can
be bolstered by encouraging engagement in leisure activities. Two specific hypotheses for this
study are as follows.
Hypothesis 3a. Individuals who transitioned to retirement or remained retired will show
lower cognitive function compared with their counterparts who remained working during a 4-
year period.
Hypothesis 3b. The impact of retirement on cognitive function will be mediated by level
of participation in leisure activities. A higher level of participation in leisure activities will
attenuate or mitigate the effect of retirement on cognitive function during a 4-year period.
Method
Data
The HRS and its supplementary data, the CAMS, were used for this study. The HRS is a
national panel survey data of adults aged 51 or older. Data collection began in 1992 and data are
now available up to 2014. HRS data include information about demographics, family structure,
62
housing, employment status, insurance, health, and cognition. The HRS features a probability
sample with oversampling of Black, Hispanic, and Floridian participants (due to high densities
and size of older adult populations; Juster & Suzman, 1995). The HRS is funded by the National
Institute on Aging.
During off years between HRS interviews, CAMS data are collected biennially from a
subsample of HRS respondents starting in 2001. This self-administered paper-and-pencil survey
allows respondents to take sufficient time to list their answers on questionnaires; collects
information about time spent engaged in various activities, household expenditures, and use of
prescription drugs; and is now available up to 2013 (Hurd & Rohwedder, 2009). This approach
has particular advantages compared to face-to-face interviews, during which respondents may
have greater time constraints to reflect on their answers. In 2001, the CAMS was mailed to 5,000
households randomly selected from the HRS 2000 survey and 3,866 questionnaires were
returned. In 2003, the CAMS was mailed to 4,156 households and 3,254 questionnaires were
returned. If couples in the same household were selected, one respondent was selected randomly
for the 2001 and 2003 waves. However, beginning in the 2005 wave, both individuals were
included in the sample if two eligible respondents were selected from the same household (Hurd
& Rohwedder, 2007). Given this shift in methodological approach, the current study used CAMS
data starting in 2005.
Study Sample
This study used the RAND HRS data file (version O), a user-friendly version of core
HRS data. HRS variables such as income, assets, and medical expenditures were cleaned and
imputed by researchers at the RAND Center for the Study of Aging with financial support from
the National Institute on Aging and Social Security Administration. Because variables used for
63
this study are from both the HRS (e.g., retirement status) and CAMS (e.g., leisure activities), the
two datasets were merged using identification numbers. The HRS is collected in even years (e.g.,
2004, 2006, 2008) and the CAMS is collected in odd years (e.g., 2005, 2007, 2009), so each
interview year of the HRS (n) and CAMS (n+1) was matched to ensure that respondents had
information for both surveys. Three waves were used for this study and are hereafter defined as
Time 1 (HRS 2004 and CAMS 2005), Time 2 (HRS 2006 and CAMS 2007), and Time 3 (HRS
2008 and CAMS 2009).
In these datasets, 4,175 respondents provided information at all three relevant waves
(Time 1, Time 2, and Time 3) of the HRS and CAMS. Among these individuals, those younger
than 51 (n = 233), who never worked or returned to work after previously retiring (n = 365), or
with cognitive impairment (as described in the Measures section) at baseline (n = 415) were
excluded. After excluding individuals with missing data for leisure activity items (n = 198) and
other study variables (n = 137), the final analytic sample was 2,827. A detailed flow chart of the
study sample is depicted in Figure 3.1.
Measures
Cognitive function. Cognitive function was the dependent variable in this study. Three
domains of cognitive function were assessed in this study: (a) memory, (b) working memory, and
(c) attention and processing speed. For memory, both immediate and delayed word recall were
measured. Respondents listened to a list of 10 nouns (e.g., book, child) and were asked to recall
as many words as possible from the list in any order. Roughly 5 minutes later (after answering
other survey questions), respondents were again asked to recall as many words from the previous
list of nouns. For immediate and delayed word recall, the score indicates the number of correct
responses (range = 0–10), leading to an overall memory score between 0 and 20. Working
64
memory is the ability to process and store information simultaneously. It is measured by a serial
7s test, which asks respondents to subtract 7 from 100 subsequently for five trials. The score
ranges from 0 to 5. For attention and processing speed, a backward counting test was used,
asking respondents to count backward for 10 continuous numbers starting at 20 (Fisher et al.,
2013; Ofstedal, Fisher, & Herzog, 2005). The total score of these measurements was calculated
(range = 0–27), with higher scores indicating better cognitive function. As previously mentioned,
individuals who scored less than 12 at baseline (Time 1) were excluded from this study because
they were regarded to be cognitively impaired. This cutoff score of 12 was based on a previous
study using the same scale (Crimmins et al., 2011). In HRS data, two additional domains of
language and orientation were assessed only among respondents aged 65 or older. Because the
present study also included respondents who were younger than 65, these two domains were not
included in cognition measures.
Retirement status. Retirement has been defined as withdrawal from the labor force
(Lazear, 1986) and thus was measured by self-report of current working status in the HRS
questionnaire (e.g., Are you currently working for pay?). For each wave, those who responded
affirmatively were considered not retired (coded as 0), whereas those who reported not currently
working for pay were considered retired (coded as 1). Previous retirement studies adopted a
similar approach to define retirement (Bonsang et al., 2012; Rohwedder & Willis, 2010). Based
on retirement status in each wave, three retirement groups were classified in this study: (a)
remained working, which refers to individuals who reported not being retired at Time 1, Time 2,
and Time 3; (b) transitioned to retirement, which refers to individuals who reported working at
Time 1 but retired at either Time 2 or Time 3; and (c) remained retired, which refers to
65
individuals who reported being retired at Time 1, Time 2, and Time 3. As previously mentioned,
participants who returned to work after previous retirement were excluded in this study.
Leisure activities. The CAMS asked respondents how much time they spent engaged in
a wide array of activities. Activities involving frequent participation (e.g., walking, reading
newspapers) were assessed in terms of hours spent during the previous week, whereas activities
involving less frequent participation (e.g., volunteering, attending religious services) were
assessed based on hours spent during the previous month. For the present study, monthly
responses were divided by 4 to be comparable to weekly responses. Twenty-six of 33 items were
further categorized into four subdomains of leisure activities: mental (seven items), physical (two
items), social (nine items), and household (eight items). Such classification is based on the face
validity and categorization described in the previous relevant literature (Adams et al., 2011;
Chang et al., 2014; Lachman et al., 2010; Paillard-Borg et al., 2009; Verghese et al., 2003).
Household activities (e.g., gardening, cleaning, or home improvements) were also defined as
leisure activities for older adults based on previous studies (Chang et al., 2014; Paillard-Borg et
al., 2009). Detailed items in each domain are presented in Table 3.1. Seven items were excluded
because they were considered not leisure but rather for survival needs (e.g., sleeping or napping,
managing a medical condition) or because most working individuals engaged in them (e.g., using
a computer, working for pay). Watching television was also excluded from this study because
previous studies showed that it is negatively related with cognitive function among older adults
(Hamer & Stamatakis, 2014; Rundek & Bennett, 2006; J. Y. J. Wang et al., 2006). For each
subdomain, items were summed to indicate total weekly time spent on leisure activities. To
minimize the loss of cases due to missing items, individuals missing data for only one item were
retained in the sample. For those missing data for more than one item in each domain (e.g., two
66
of seven items missing for mental activities) were excluded across all four domains (n = 198).
Due to the non-normal distribution of all four domains of leisure activities, each domain was
dichotomized at its median value. Values lesser than the median were considered to reflect a
lower level of participation in activities (coded as 0), whereas values greater than the median
were considered indicative of higher level of participation in activities (coded as 1). This
approach was applied to all four domains of leisure activities. Leisure activities at Time 2 were
included as mediators in this study, whereas leisure activities at Time 1 were included as control
variables.
Control variables. Baseline (Time 1) variables were controlled in the analysis as
follows.
Depressive symptoms. Depressive symptoms were measured with a modified 8-item
subscale from the Center for Epidemiologic Studies Depression Scale. The measure asked
whether respondents felt (a) depressed, (b) that everything was an effort, (c) their sleep was
restless, (d) they could not get going, (e) lonely, (f) they enjoyed life (reverse coded), (g) sad,
and (h) happy (reverse coded) much of the time during the previous week. Higher scores
indicated more depressive symptoms (range = 0–8).
Self-rated health. Self-rated health was measured in one item with a 5-point scale:
“Would you say your health is excellent, very good, good, fair, or poor?” After reverse coding,
higher scores indicated better self-rated health.
Functional limitations. Functional ability was measured by five specific instrumental
activities of daily living: shopping for groceries, preparing a hot meal, using a phone, managing
money, and taking medication (1 = some difficulty, 0 = no difficulty). These five items were
summed to create a composite count. Because a majority of responses were 0, this variable was
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dichotomized to indicate any difficulties (coded as 1) or no difficulties (coded as 0) regarding
functional limitations.
Health behaviors. Health behaviors were measured by respondents’ use of alcohol (0 =
never, 1 = light drinker, 2 = heavy drinker) and cigarettes (0 = never, 1 = previous smoker, 2 =
current smoker). Men who consume alcohol on one or more days a week and have three or more
drinks per occasion and women who consume two or more drinks per occasion were considered
heavy drinkers. Responses between no use and heavy use were classified as light drinkers. This
classification is based on previous studies (Lyu & Lee, 2012; Satre, Gordon, & Weisner, 2007).
Sociodemographic factors. Age (years), gender (0 = female, 1 = male), race and ethnicity
(0 = non-Hispanic White, 1 = non-Hispanic Black, 2 = Hispanic, 3 = other; dummy coded),
education (years), marital status (0 = unmarried, i.e., divorced, separated, widowed, or never
married; 1 = married or partnered), and household wealth (log transformed) were also included
as control variables in the present study.
Baseline cognitive function and leisure activities. Cognitive function (range = 12–27)
and four domains of leisure engagement (0 = low, 1 = high) at baseline were included in the
model as control variables.
Data Analysis
Preliminary analyses were conducted using Stata software. Analysis of variance and chi-
square tests were conducted to explore whether significant differences existed in major study
variables by three retirement groups: remained working, transitioned to retirement, and remained
retired. Finally, path analysis was conducted to test the impact of retirement on cognitive
function via leisure activity participation. Specifically, retirement status was inserted as an
independent variable in the model. Transitioned to retirement and remained retired groups were
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included as dummy variables, with the referent being the remained working group. Four domains
of leisure engagement (each binary measure: 0 = low, 1 = high) at Time 2 were included as
multiple mediators while controlling for leisure engagement at Time 1. Cognitive function at
Time 3 was included as a dependent variable while controlling for cognitive function at Time 1.
Age, age-squared, gender, race and ethnicity, education, marital status, household wealth (log),
self-rated health, depressive symptoms, functional limitations, and smoking and drinking
behaviors at baseline were controlled as covariates. Correlations between the four mediators
were also indicated as separate commands in the model. Goodness-of-fit indexes including chi-
square,
comparative fit index (CFI), and root mean square error of approximation (RMSEA)
were used as criteria for estimating the model (Barrett, 2007). A minimum value of .90 for the
CFI and values less than .05 for RMSEA were considered to indicate good model fit for this
study. Path analysis was conducted with the weighted least-squares parameter estimator to
estimate categorical mediators or outcome variables. A bootstrapping method (1,000 iterations)
was applied to obtain bias-corrected confidence intervals (CIs) for both direct and indirect effects
using Mplus software (Muthén & Muthén, 2010).
Results
Characteristics of Sample by Retirement Status
Table 3.2 provides mean and frequencies of major study variables by retirement status. In
this sample of 2,827 individuals during the study period, a majority of participants (n = 1,495,
52.88%) remained retired, whereas 933 (33.00%) remained working and 399 (14.11%)
transitioned from work to retirement. The p-values presented in the last two columns of Table
3.2 are based on chi-square and analysis of variance tests and indicate any significant differences
in major study variables across retirement groups. All study variables were compared between
69
(a) remained working and transitioned to retirement and (b) remained working and remained
retired.
Results indicate cognitive function (Time 3) significantly differed across retirement
groups. Compared to the remained working group (M = 17.39, SD = 3.35), those who
transitioned to retirement (M = 16.53, SD = 3.63) and remained retired (M = 15.68, SD = 3.69)
showed significantly lower cognitive function (p < .001). At baseline (Time 1), this difference
also existed across retirement groups. Compared to those who remained working (M = 17.86, SD
= 2.88), participants who transitioned to retirement (M = 17.31, SD = 2.81, p < .01) or remained
retired (M = 16.70, SD = 2.92, p < .001) showed significantly lower cognitive function. In terms
of leisure activity participation at Time 2, the remained working group had the smallest
proportion of individuals with high engagement in mental activities (41.80%) compared to the
other two groups (transitioned to retirement: 50.13% and remained retired: 55.52%). No
significant differences existed in the proportion of physical activity participation across
retirement groups. For social activities, those in the remained working group were less likely to
engage in higher social participation (45.55%) compared to remained retired group (52.58%).
For household activities, 44.69% of the remained working group reported high participation,
which was only significantly lower compared to the remained retired group (53.71%).
For other covariates at Time 1, compared to those who remained working, individuals
who transitioned to retirement were older (p < .001), had lower education (p < .01), and had
higher depressive symptoms (p < .05). Those who remained retired were older (p < .001); more
likely to be female (p < .001), non-Hispanic White (p < .01), and unmarried (p < .01); had lower
education (p < .001), higher depressive symptoms (p < .001), and with functional limitations (p <
70
.001); and were more likely to never consume alcohol (p < .001) than their counterparts who
remained working (see Table 3.2).
Path Models for Retirement, Leisure Activities, and Cognition
Figure 3.2 shows the model fit indexes and specific direct paths between variables. The
explained variance for cognitive function was .345. The correlations between domains of leisure
activities are also presented in Figure 3.2. The fit indexes indicated good fit, CFI = .979,
RMSEA = .033 (CI = .025, .042). Although 2
(16) = 66.335 (p < .001) was statistically
significant, this is not an uncommon result for studies with large sample size. Larger sample
sizes are considered to increase the likelihood of poor model fit when using chi-square as a
goodness-of-fit test (Barrett, 2007).
The significance of unstandardized coefficients and standard errors for each pathway is
shown in Figure 3.2. Solid lines indicate statistically significant direct paths, whereas dashed
lines indicate nonsignificant paths. Retirement was negatively related with cognitive function
(Time 3) after controlling for other covariates including baseline cognitive function (Time 1).
Specifically, those who remained retired showed significantly lower levels of cognitive function
(b = -0.321, SE = 0.164, p < .05) compared to the remained working group. However, the
transitioned to retirement group did not show significant difference in cognitive function
compared to the remained working group.
The path result for the impact of leisure activity engagement (Time 2) on cognitive
function (Time 3) showed that only mental activities were positively related with cognitive
function. Individuals with higher level of engagement in mental activities (b = 0.191, SE = 0.075,
p < .01) had better cognitive function compared to those with lower level of engagement in these
71
activities. However, having a high level of engagement in physical, social, and household
activities did not significantly affect cognitive function.
Transition to retirement was positively associated with a higher level of engagement in
mental (b = 0.221, SE = 0.083, p < .05) and social (b = 0.194, SE = 0.081, p < .05) activities
compared to the remained working group. Remaining retired was positively associated with
higher engagement in mental (b = 0.234, SE = 0.070, p < .01), social (b = 0.208, SE = 0.069, p <
.01), and household (b = 0.197, SE = 0.072, p < .01) activities. However, physical activities were
not influenced by retirement status.
Testing the Significance of Indirect Effects
Table 3.3 shows the statistical significance of the indirect effect of retirement on
cognitive function through leisure engagement. Both total indirect and specific indirect effects
are presented with 95% bias-corrected, bootstrapped CIs. The total indirect effect of transition to
retirement on cognitive function was statistically significant through one mediator (mental
activities) (b = 0.058; 95% CI = 0.014, 0.123; p < .05). The specific indirect effect of mental
activities (b = 0.042; 95% CI = 0.009, 0.108; p < .05) was significant. Similarly, the total indirect
effect (b = 0.061; 95% CI = 0.018, 0.126; p < .05) of remaining retired on cognitive function was
statistically significant through the specific indirect effect of mental activity engagement (b =
0.045; 95% CI = 0.012, 0.107; p < .05). However, physical, social, and household activities were
not significant mediators in the relationship between retirement and cognitive function.
Discussion
This is one of the first studies to examine the mediating effect of leisure activities in the
relationship between retirement and cognition using national longitudinal data (HRS and
supplementary CAMS data), specifically using information from 2,827 older adults aged 51 or
72
older during a 4-year period while controlling for both baseline cognitive function and leisure
activity participation. Results indicate a negative association between retirement (remained
retired only) and cognition (supporting Hypothesis 3a). Moreover, this relationship was
attenuated by engaging more in mental activities (e.g., reading newspapers, reading books,
playing cards or games, solving puzzles, doing arts and crafts, listening to music, singing or
playing music, and praying or mediating), partially supporting Hypothesis 3b, as evidenced by
the significant indirect path from retirement to cognition via mental activities. However,
physical, social, and household activities had no significant effect on this path.
This finding suggests a negative impact of retirement on cognition, which supports the
first hypothesis and previous retirement–cognition studies (Bonsang et al., 2012; Rohwedder &
Willis, 2010). Cessation of a previous role as a worker may have decreased brain stimulation
upon retirement. Rohwedder and Willis (2010) referred to this as the “unengaged lifestyles
hypothesis” of mental retirement as one way to explain why retirement might cause cognitive
decline (i.e., retirees engage less in a cognitively stimulating environment than workers). This
may explain lower cognitive function among those in the remained retired group compared to
their counterparts who remained working.
Although the magnitude of mental activities was not sufficient to offset the negative
impact of retirement on cognition, the significant indirect effect of mental activities implies a
protective role of mental stimulation in the retirement–cognition relationship. Specifically, this
finding implies that activities such as reading, playing card games, or doing puzzles may play a
significant role in reducing the retirement effect on cognition, partly supporting the second
hypothesis. However, other domains of activities (physical, social, and household) had no direct
or indirect effects on cognition. Indeed, previous studies have shown more consistent benefits of
73
mental activities, compared to less consistent results regarding physical or social activities in
terms of cognition (Verghese et al., 2003; J. Y. J. Wang et al., 2006; Wilson et al., 2002). For
example, Verghese et al. (2003) examined the role of cognitive and physical activities on
dementia risk regardless of retirement status and found that only cognitive activities reduced risk
of dementia. In addition, J. Y. J. Wang et al. (2006) found that only cognitive leisure activities
were related with reduced risk of cognitive impairment but not physical or social activities. The
authors further noted that “the association between cognitive activity and the reduced risk of
cognitive impairment may reflect mental stimulation rather than a nonspecific result of being
active” (p. 913). Thus, among various leisure activities, activities that are directly related to brain
stimulation may most benefit cognition.
The present study has several implications for older Americans who experience
retirement. Because the negative impact of retirement on cognition could be mitigated by
actively engaging in mental activities, promoting leisure engagement, specifically focusing on
mental activities at the community level (e.g., adult day care center, retirement community), may
improve cognition in this population. From a policy perspective, more funding to support
nonpharmacological interventions for cognitive programs is necessary to enhance cognitive
function (La Rue, 2010). Thus far, several randomized controlled trials of cognitive training
programs such as Advanced Cognitive Training for Independent and Vital Elderly (Willis et al.,
2006) or Improvement in Memory with Plasticity-Based Adaptive Cognitive Training with
computer-based brain fitness (Smith et al., 2009) among cognitively healthy older adults have
shown benefits in terms of cognitive skills (e.g., processing speed). Nevertheless, La Rue (2010)
argued that “training benefits were task-specific and usually did not extend to apparently similar,
more naturalistic cognitive tasks (e.g., remembering a shopping list rather than a list of unrelated
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words of the type used in training)” (p. 104). Thus, developing a cognitive training program
whose benefit expands to the daily lives of older retirees should be pursued. Moreover, creating
mentally stimulating activities (e.g., memory games) using smartphones or smartwatches for the
baby boomer generation will provide a tech-friendly approach to retaining cognitive function
among older adults in the present era.
Limitations
The present study featured several notable limitations. First, the endogeneity of
retirement decisions was not controlled for in this study. Although this study excluded
individuals who were cognitively impaired at baseline and controlled for baseline cognitive
function, individuals experiencing greater cognitive decline might have been more likely to retire
than their cognitively healthier counterparts. Second, the categorization of leisure domains was
based on previous studies, but these distinctions may still be ambiguous. For example, some
domains may share same items (e.g., playing a musical instrument can be both mental and
physical). In addition, one activity may be more demanding than others in the same domain (e.g.,
playing sports may be physically more demanding than walking). Indeed, some studies assigned
weights for leisure activities, although this may also be difficult because weights for an item may
vary based on the study sample (Verghese et al., 2003). Also, using seven items (e.g., reading,
doing puzzles, doing arts and crafts) to assess mental activities may not have been sufficient to
inform interventions or specific programs. Unfortunately, no common tool has been used to
measure cognitive activities, and each relevant study measured mental activities (e.g., specific
activities to include, intensity, number of activities) in its own way. Thus, more effort is
necessary in future research to establish a standard measure for level of engagement in mental
activities with improved validity and reliability (La Rue, 2010). Last, the CAMS survey does not
75
ask about the specific context of activity participation (e.g., whether the activity was pursued
alone or with other people, whether individuals chose to engage in an activity), instead focusing
solely on involvement (Morrow-Howell et al., 2014). Thus, greater consensus in this field of
study to validly measure leisure is most needed to generate more consistent results. Future
studies that test how to classify leisure activities or incorporate the context of leisure activities in
survey questionnaires will be necessary.
Conclusions
This study suggests future studies investigating whether factors such as socioeconomic
status and health conditions provide a better picture of the mechanisms among retirement, mental
activities, and cognitive function. For example, individuals with higher household wealth or
better life satisfaction may be able to engage in higher levels of mental activity compared to their
counterparts. This will delineate whether the path from retirement to cognitive function via
mental activities differs among individuals with distinguishable levels of socioeconomic status or
health conditions.
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Table 3.1. Subdomains of Leisure Activities from the CAMS
Mental (7 Items) Physical (2 Items) Social (9 Items) Household (8 Items)
Reading newspapers or
magazines
Reading books
Playing cards or games, or
solving puzzles
Doing arts and craft projects,
including knitting, embroidery,
or painting
Listening to music
Singing or playing a musical
instrument
Praying or meditating
Walking
Participating in sports or other
exercise activities
Visiting in person with friends,
neighbors, or relatives
Communicating by telephone,
letters, or email with friends,
neighbors, or relatives
Helping friends, neighbors, or
relatives
Physically showing affection
for others through hugging,
kissing, etc.
Doing volunteer work for
religious, educational, health-
related, or other charitable
organizations
House cleaning
Preparing meals and cleaning
up afterward
Washing, ironing, or mending
clothes
Shopping or running errands
Home improvements,
including painting,
redecorating, or making home
repairs
Yard work or gardening
Working on, maintaining, or
cleaning a car or vehicle
Caring for pets
77
Attending religious services
Attending meetings of clubs or
religious groups
Attending concerts, movies, or
lectures, or visiting museums
Dining or eating outside the
home (not related to business
or work)
Note: CAMS, Consumption and Activities Mail Survey; 7 items (i.e., “watching television,” “sleeping and napping,” “grooming and hygiene,” “using computer,”
“working for pay,” “taking care of finances or investments, such as banking, paying bills, balancing the checkbook, doing taxes,” and “self-treating or self-
managing an existing medical condition”) were excluded from this study.
78
Table 3.2. Sample Characteristics by Retirement Status (N = 2,827)
Working
a
Transition
b
Retired
c
Working vs.
Transition
d
Working vs.
Retired
d
(n = 933) (n = 399) (n = 1,495)
Range n (%) or M (SD) n (%) or M (SD) n (%) or M (SD) p p
Age (years) 51–93 59.41 (6.77) 64.28 (6.71) 69.64 (8.05) < .001 < .001
Gender .546 < .001
Female 499 (53.48) 229 (57.39) 947 (63.34)
Male 434 (46.52) 170 (42.61) 548 (36.66)
Race and ethnicity .860 < .01
White 784 (84.03) 335 (83.96) 1,322 (88.43)
Black 74 (7.93) 42 (10.53) 94 (6.29)
Hispanic 53 (5.68) 18 (4.51) 59 (3.95)
Other 22 (2.36) 4 (1.00) 20 (1.34)
Marital status .463 < .01
Married or partnered 709 (75.99) 288 (72.18) 1,046 (69.97)
79
Unmarried
e
224 (24.01) 111 (27.82) 449 (30.03)
Education 0–17 13.89 (2.52) 13.41 (2.51) 12.94 (2.54) < .01 < .001
Household wealth ($1,000) -100.81–26,040 522.99 (1,291.13) 452.37 (862.66) 505.72 (815.74) .719 1.000
Depression 0–8 0.81 (1.43) 1.05 (1.65) 1.22 (1.79) < .05 < .001
Functional limitation 1.000 < .001
None 909 (97.43) 386 (96.74) 1,366 (91.37)
Any 24 (2.57) 13 (3.26) 129 (8.63)
Alcohol use .911 < .001
Never 325 (34.83) 153 (38.35) 701 (46.89)
Light 466 (49.95) 188 (47.12) 618 (41.34)
Heavy 142 (15.22) 58 (14.54) 176 (11.77)
Cigarette use .323 1.000
Never 419 (44.91) 156 (39.10) 646 (43.21)
Former 392 (42.02) 188 (47.12) 680 (45.48)
Current 122 (13.08) 55 (13.78) 169 (11.30)
Leisure engagement
80
Mental activity < .05 < .001
High engagement 390 (41.80) 200 (50.13) 830 (55.52)
Physical activity .867 .330
High engagement 441 (47.27) 176 (44.11) 657 (43.95)
Social activity .153 < .01
High engagement 425 (45.55) 205 (51.38) 786 (52.58)
Household activity .413 < .001
High engagement 417 (44.69) 196 (49.12) 803 (53.71)
Cognition, Time 3 0–27 17.39 (3.35) 16.53 (3.63) 15.68 (3.69) < .001 < .001
Cognition, Time 1 12–27 17.86 (2.88) 17.31 (2.81) 16.70 (2.92) < .01 < .001
a
Remained working group.
b
Transition to retirement group.
c
Remained retired group.
d
P-values for chi-square and analysis of variance tests.
e
Unmarried status included participants who were divorced, widowed, separated, or never married.
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Table 3.3. Indirect Effects of Retirement on Cognitive Function through Leisure Activity
Estimate 95% CI
Transition to retirement
Total indirect effect 0.058* 0.014, 0.123
Mental activity 0.042* 0.009, 0.108
Physical activity 0.000 -0.011, 0.016
Social activity 0.015 -0.009, 0.065
Household activity 0.000 -0.021, 0.028
Remained retired
Total indirect effect 0.061* 0.018, 0.126
Mental activity 0.045* 0.012, 0.107
Physical activity -0.001 -0.017, 0.006
Social activity 0.016 -0.011, 0.058
Household activity 0.000 -0.029, 0.032
Note. CI values represent 95% bias-corrected, bootstrapped confidence intervals.
*CI does not include zero and thus is significant at p < .05.
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Figure 3.1. Sample Selection Criteria for the Present Study
Note. Wave 1: HRS 2004 and CAMS 2005; Wave 2: HRS 2006 and CAMS 2007; Wave 3: HRS 2008 and CAMS
2009.
Excluded respondents younger
than 51: n = 233
Excluded individuals who never
worked or went back to work
after previously retiring during
the study period: n = 365
Wave 1, 2, 3
N = 4,175
n = 3,942
n = 3,577
Excluded individuals with
cognitive impairment (score <12)
at baseline: n = 415
n = 3,162
Excluded respondents missing in
more than one item in each
leisure activity domain: n = 198
n = 2,964
Excluded missing cases in the
study variables: n = 137
n = 2,827
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Figure 3.2. Path Analysis of Retirement, Leisure Activity Engagement, and Cognition
Note. Model fit indexes: χ
2
(16) = 66.335, p < .001; RMSEA = .033 (CI = .025, .042); CFI = .979. Explained
variance is provided in bold above cognitive function. Solid lines indicate significant unstandardized parameter
estimates (SE).
*p < .05. **p < .01.
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Chapter 4: Widowhood and Cognition among Older Adults: The Role of Leisure Activities
Introduction
Cognitive function is a crucial aspect of successful aging (Rowe & Kahn, 1997) that
enables older individuals to sustain better psychological well-being (Llewellyn et al., 2008) and
independence in later life. According to cognitive reserve theory, individuals with more
cognitive reserve have greater resilience to brain damage and thus, manifest less cognitive
deficits than their counterparts (Scarmeas & Stern, 2003; Y. Stern, 2002). This theory posits
innate intelligence and life experiences (e.g., education, occupational attainment, leisure
activities) may supply cognitive reserve and help individuals cope with brain pathology such as
Alzheimer’s disease or other dementias. Previous empirical studies have also shown that more
education (Gatz et al., 2001; Y. Stern et al., 1994), complex occupation attainment (Andel,
Silverstein, et al., 2015), later-life leisure activity engagement (Lee & Chi, 2016; Podewils et al.,
2005; Verghese et al., 2003; J. Y. J. Wang et al., 2006) are associated with less cognitive decline
or lower risk of dementia. Among these factors, however, leisure activity participation is
receiving much greater attention in recent years because it is modifiable in later life.
Losing a loved one is considered one of the most stressful life events in later life. The
transition to widowhood naturally entails the process of grieving and challenges the surviving
partner with loneliness and the need to seek a new identity once ritual supports are removed
(Balkwell, 1981). Indeed, the majority of previous widowhood studies have focused on physical
and mental health outcomes (e.g., mortality, suicidal risk, depression) of widowed individuals in
their process of coping with grief (Li, 2005; Stroebe et al., 2007). However, fewer studies
examined the effects of widowhood specifically on cognitive function (Rosnick et al., 2010).
Moreover, the few existing studies have shown mixed results. Among relevant cross-sectional
85
studies, Feng et al. (2014) found that among Chinese older adults, being widowed was associated
with higher risk of cognitive impairment compared to married individuals, but only among men.
Xavier, Ferraz, Trentini, Freitas, and Moriguchi (2002) found that grief after loss negatively
affected memory among Brazilian oldest-old adults. Ward et al. (2007) replicated Xavier et al.’s
(2002) study but found that after controlling for mood (e.g., depression anxiety), no significant
relationship existed between bereavement and cognitive functioning.
Among longitudinal studies, research has found that widowed people have higher risk of
dementia (Sundström, Westerlund, Mousavi-Nasab, Adolfsson, & Nilsson, 2014), cognitive
impairment (Håkansson et al., 2009), and greater cognitive decline (Aartsen et al., 2005; van
Gelder et al., 2006) compared to their married counterparts. On the other hand, Helmer et al.
(1999) found higher risk of dementia only among never-married individuals compared to their
married counterparts, but not among widowed individuals. Similarly, Comijs et al. (2011) found
that spousal loss was not significantly associated with cognitive decline, whereas illness of a
partner was associated with better cognitive function. Such equivocal results regarding the
relationship between widowhood and cognitive function leave the question of whether any other
factors moderate these relationships unanswered.
According to the stress-buffering hypothesis (Cohen & Wills, 1985; Coleman & Iso-
Ahola, 1993), leisure activities may attenuate the negative effects of life events stress on health.
Indeed, leisure activities have been considered one type of resources to cope with negative life
events, including losses such as death of significant loved ones (Coleman & Iso-Ahola, 1993;
Kleiber et al., 2002). Leisure activities is also considered to provide social support to individuals
to reduce feelings of loneliness or perceived social isolation via companionship (Coleman & Iso-
Ahola, 1993). Because widowed individuals may lack a previous leisure partner (Patterson,
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1996) or vital companion in their social network, activity engagement (e.g., contact with or
visiting children or other relatives) may be a crucial factor for successful adaptation to
widowhood (Isherwood, King, & Luszcz, 2012). Leisure activities may also provide new roles
and feelings among widowed individuals (Bennett et al., 2010), which may further generate self-
restoration or reconstruction of meaning of life (Kleiber et al., 2002) and provide a sense of
purpose and social integration (Silverstein & Parker, 2002). In this respect, leisure activities may
serve as buffer against cognitive decline influenced by widowhood.
Nevertheless, this coping resource may not be pursued by every individual to a similar
degree after widowhood. Indeed, Utz et al. (2002) found that widowed individuals participated
more in informal social activities (e.g., getting together or talking on the phone with friends,
neighbors, or relatives) compared to their nonwidowed counterparts. However, these authors
further suggested that “not all widowed persons have the same resources to alter their levels of
social participation” (p. 522). Likewise, Janke, Nimrod, et al. (2008) studied changes in leisure
involvement during the transition to widowhood and noted that “while the majority of widows in
this sample reduced their involvement in leisure activity and appear to disengage, it is also
important to point out that a significant number of widows did maintain or even increase their
activity levels” (p. 96). Lee Min, and Chi (2017) found that compared to married individuals,
widowed men engaged less in social activities but not women. In this respect, the level of leisure
activity participation during widowhood may vary even among widowed persons. Some
individuals may engage more in leisure and better adjust to widowhood by maintaining better
cognitive function compared to those who engage less in leisure activities. Thus, further
examination is needed to delineate whether there is a potential buffering effect of leisure
engagement on the relationship between widowhood and cognitive function.
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However, limited studies have directly examined the buffering effect of leisure activity
participation in the relationship between widowhood and cognitive function. Most relevant
leisure studies examined the role of leisure in the impact of widowhood on health outcomes such
as quality of life (Silverstein & Parker, 2002) and functional health (Unger et al., 1997), but not
specifically cognitive function. Other studies examined the main effects of widowhood and
leisure activities on cognitive function but have not further tested the moderating effect of leisure
activities. For example, Feng et al. (2014) studied the association among widowhood, social and
productive activities, and cognitive function and found that widowed status was associated with
higher risk, whereas social activity engagement was associated with lower risk of cognitive
impairment among Chinese older adults. However, the moderating effect of social activities was
not further investigated.
Moreover, the majority of relevant studies on widowhood and cognition were conducted
outside of the United States, using older adult samples from Sweden (Sundström et al., 2014),
Finland, Netherlands, Italy (Aartsen et al., 2005; Comijs et al., 2011; Håkansson et al., 2009; van
Gelder et al., 2006), France (Helmer et al., 1999), Brazil (Xavier et al., 2002), and China (Feng et
al., 2014), making it difficult to generalize results to U.S. older adults because the cultural
context during widowhood (e.g., degree of support received from adult children) may differ
across countries. In addition, Carr and Utz (2001) noted several methodological issues in the past
widowhood studies, not limited to but including (a) using a widowed-only sample, which makes
it difficult to compare results to nonbereaved peers; (b) using cross-sectional data, which do not
capture preloss characteristics of the sample; and (c) using long-term data, which may
underestimate the short-term effects of widowhood. Therefore, examining mechanisms among
widowhood, leisure activities, and cognitive function using a U.S. longitudinal sample of older
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persons including not only those who remained married or remained widowed but also those who
transitioned to widowhood during an appropriate time span seems necessary as the next step in
this field of study.
With this aim, this study examined the impact of widowhood on cognitive function
among older adults during a 4-year period using national longitudinal data from the HRS
matched with supplementary data from the CAMS, which asks about participation in a wide
array of activities among older Americans. This study further delineated the moderating role of
leisure activity participation in the widowhood–cognition relationship. This study sought to help
individuals who experience widowhood by identifying whether specific leisure activities benefit
cognitive function in this population following widowhood. The hypotheses for this study are as
follows.
Hypothesis 4a. Individuals who transitioned to widowhood or remained widowed will
show lower level of cognitive function compared to those who remained married.
Hypothesis 4b. The negative impact of widowhood on cognitive function will be
moderated by the level of leisure activity engagement.
Method
Data
This study used HRS data and supplementary CAMS data. The HRS is a national panel
survey collected biennially since 1992 (now available up to 2014) among adults aged 51 or older.
The HRS survey collects a wide array of information including participant demographics, family
structure, health, employment, housing, and cognition (Juster & Suzman, 1995). Whereas the
HRS is collected in even years, CAMS data are collected in odd years between core HRS survey
years beginning in 2001 (now available up to 2013; Hurd & Rohwedder, 2009). The CAMS
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collects data on time spent on activities, household expenditures, and prescription drugs. In 2001,
the CAMS was mailed to 5,000 random households interviewed in the HRS 2000 survey and
3,866 questionnaires were returned. In 2003, the CAMS was mailed to 4,156 households and
3,254 questionnaires were returned. If a household had two eligible respondents, only one
respondent was chosen to participate. Starting in 2005, CAMS was configured differently. If two
eligible respondents were present in the same household, both were selected to participate in the
CAMS (Hurd & Rohwedder, 2007). Due to this different sampling approach, the present study
used CAMS data starting in 2005.
Study Sample
For this study, the RAND HRS data file (version O) was merged with CAMS data.
RAND HRS is a cleaned version of core HRS data with several major imputed variables,
including income, assets, and medical expenditures. Three waves of RAND HRS and CAMS
data were used in this study after individually matching respondents using identification
numbers. Because the HRS was collected in even years (e.g., 2004, 2006, 2008) and the CAMS
was collected in odd years (e.g., 2005, 2007, 2009), each interview year for HRS (n) and CAMS
(n+1) were matched to assure that respondents were present in both dataset. Hereafter, these
waves are referred to as Time 1 (HRS 2004 and CAMS 2005), Time 2 (HRS 2006 and CAMS
2007), and Time 3 (HRS 2008 and CAMS 2009) in this study.
In this sample, 4,175 respondents were present in the HRS and CAMS data for Times 1,
2, and 3. Among these individuals, those younger than 51 (n = 233); who reported being
divorced, separated, never married, or remarried after previous widowhood (n = 652); and who
were cognitively impaired (as described in the Measures section; n = 392) at baseline were
excluded from this study. After excluding participants with missing data for leisure activity items
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(n = 88) and other study variables (n = 192), the final analytic sample was 2,618. A detailed flow
chart of the study sample is depicted in Figure 4.1.
Measures
Cognitive function. Cognitive function at Time 3 was the dependent variable in this
study. Three cognitive domains (memory, working memory, and attention and processing speed)
were measured to indicate cognitive function. For memory, immediate and delayed word recall
were tested. Respondents listened to a list of 10 nouns (e.g., book, child) and were asked to recall
as many words as possible (a) immediately and (b) roughly 5 minutes after answering other
survey questions. A subsequent memory score reflected the number of correct responses ranging
from 0 to 20. For working memory, a serial 7s test was conducted by asking respondents to
subtract 7 from 100 subsequently for five trials. The working memory score ranged from 0 to 5.
For attention and processing speed, a backward counting test was conducted by asking
respondents to count backward for 10 continuous numbers starting at 20 (range = 0–2; Fisher et
al., 2013; Ofstedal et al., 2005). The total cognition score was calculated by summing the three
domains; higher scores indicated better cognitive function (range = 0–27). Baseline cognitive
function was controlled in this study, and those who scored less than 12 at baseline were
excluded because they were regarded as cognitively impaired. This cutoff value of 12 was based
on the recommendation of a previous study using the same HRS data (Crimmins et al., 2011).
Widowhood status. Widowhood was measured by self-report of current marital status in
the HRS questionnaire. Based on marital status from each wave, three widowhood groups were
created in this study: (a) remained married, which refers to individuals who reported being
married or partnered at Time 1, Time 2, and Time 3; (b) transitioned to widowhood, which refers
to individuals who reported being married or partnered at Time 1 and widowed at either Time 2
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or Time 3; and (c) remained widowed, which refers to individuals who reported being widowed
at Time 1, Time 2, and Time 3. As previously mentioned, individuals who reported being
remarried after previous widowhood were excluded.
Leisure activities. The CAMS asked respondents to report hours spent engaged in 33
activities. Activities considered to involve frequently participation (e.g., reading newspapers)
were assessed in terms of hours spent per week, whereas those involving less frequent
participation (e.g., doing volunteer work) were assessed in terms of hours spent per month (Hurd
& Rohwedder, 2009). To make both reference periods comparable, monthly activities were
divided by 4 and interpreted as weekly responses. Activities that were not classified as leisure
(e.g., sleeping or napping, managing a medical condition) and watching TV, which has been
reported to have a negative impact on cognition (Hamer & Stamatakis, 2014; Rundek & Bennett,
2006; J. Y. J. Wang et al., 2006), were excluded from this study. The remaining 26 items were
categorized into four domains (mental: seven items; physical: two items; social: nine items;
household: eight items) based on face validity and previous relevant literature (Adams et al.,
2011; Chang et al., 2014; Lachman et al., 2010; Paillard-Borg et al., 2009; Verghese et al.,
2003). The detailed categorization of these items is presented in Table 4.1. Finally, items were
summed to indicate total hours spent per week in each domain of leisure activities. Due to the
non-normal distribution of total hours, each domain was dichotomized at its median to indicate
high (coded as 1) or low (coded as 0) level of participation in leisure activities. Leisure activities
at Time 2 were included in this study.
Control variables. The following baseline variables were controlled in the analyses.
Depressive symptoms. Depressive symptoms were measured with a modified 8-item
version of the Center for Epidemiologic Studies Depression Scale. The measure asked whether
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respondents felt (a) depressed, (b) that everything was an effort, (c) their sleep was restless, (d)
they could not get going, (e) lonely, (f) enjoyed life (reverse coded), (g) sad, and (h) happy
(reverse coded) much of the time during the previous week. Higher scores indicated more
depressive symptoms (range = 0–8).
Self-rated health. Self-rated health was measured by one item with a 5-point scale:
“Would you say your health is excellent, very good, good, fair, or poor?” After reverse coding,
higher scores indicated better self-rated health.
Functional limitations. Functional ability was measured by five instrumental activities of
daily living: shopping for groceries, preparing hot meal, using a phone, managing money, and
taking medication (1 = some difficulty, 0 = no difficulty). These five scores were summed to
create a total score of functional limitations. Due to a majority of zeros for the total count, this
variable was dichotomized to indicate whether respondents had any difficulties (coded as 1) or
no difficulties (coded as 0) in functioning.
Health behaviors. Health behaviors were measured by respondents’ use of alcohol (0 =
never, 1 = light drinker, 2 = heavy drinker) and cigarettes (0 = never, 1 = previous smoker, 2 =
current smoker). Men who consumed alcohol on one or more days a week and had three or more
drinks per occasion and women who consumed two or more drinks per occasion were considered
heavy drinkers. Responses between those who never consumed alcohol and heavy drinkers were
considered light drinkers. This gender-specific classification was based on previous studies (Lyu
& Lee, 2012; Satre et al., 2007).
Presence of living children. Because having children was often considered a proxy for
social support in previous relevant studies (Sundström et al., 2014), this study assessed whether
respondents had living children (0 = none, 1 = at least one).
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Sociodemographic factors. Age (years), gender (0 = female, 1 = male), race and ethnicity
(0 = non-Hispanic White, 1 = non-Hispanic Black, 2 = Hispanic, 3 = other; dummy coded),
education (years), retirement status (0 = working, 1 = retired), and household wealth (log
transformed) were also included as control variables in the present study. Living arrangement (0
= living with someone, 1 = living alone) was initially included, but due to its high correlation (r =
.82) with widowhood status, it was ultimately excluded.
Data Analysis
Preliminary analyses were conducted to assess sample characteristics by widowhood
groups. Specific group differences were further tested using chi-square and analysis of variance
tests: (a) remained married versus transitioned to widowhood and (b) remained married versus
remained widowed. Finally, multiple regression analyses were conducted to first test the main
effect of widowhood status and leisure engagement on cognitive function. Then, the interaction
effects of four domains of leisure activities and widowhood status on cognitive function were
tested. Each model controlled for age, age-squared, gender, race and ethnicity, education,
retirement status, presence of living children, household wealth (log), self-rated health,
depressive symptoms, functional limitations, smoking and drinking behaviors, and baseline
cognitive function. All analyses were conducted using Stata software (v.12.0). No
multicollinearity issues emerged in any of these analyses.
Results
Sample Characteristics
Table 4.2 presents descriptive information for the study sample stratified by the three
widowhood groups. All variables were measured at Time 1 except for leisure activity
engagement at Time 2 and cognitive function at Time 3. About 79.14% of individuals in this
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sample had remained married, whereas 4.66% transitioned to widowhood and 16.20% remained
widowed during the study period. The last two columns in the table show whether significant
group differences existed between (a) remained married and transitioned to widowhood groups
and (b) remained married and remained widowed groups. Compared to those who remained
married, individuals who transitioned to widowhood were more likely to be older (p < .001),
female (p < .001), retired (p < .01), less educated (p < .01), and have higher depressive
symptoms (p < .01) at Time 1. No significant differences existed in terms of leisure activity
engagement at Time 2 and cognitive function at both Time 1 and Time 3.
Compared to those who remained married, individuals who remained widowed were
more likely to be older (p < .001), female (p < .001), retired (p < .001), less educated (p < .001);
have lower household wealth (p < .001), higher depressive symptoms (p < .001), and more
functional limitations (p < .01); never consume alcohol (p < .001); and live without children (p <
.05) at Time 1. Individuals who remained widowed had lower cognitive function at both Time 1
and Time 3 compared to those who remained married (p < .001). With regard to leisure activity
engagement, individuals who remained widowed showed higher levels of engagement in mental
activities (p < .001), and lower levels of engagement in physical (p < .001) and household (p <
.05) activities at Time 2 compared to their counterparts who remained married.
Widowhood, Leisure Activity Engagement, and Cognitive Function
Table 4.3 shows the multiple regression analyses of widowhood, leisure activity
engagement, and cognitive function testing the main effects of study variables on cognition.
First, each domain of leisure activities was included in the regression analysis (Model 1: mental,
Model 2: physical, Model 3: social, Model 4: household); then, all four domains were included
simultaneously (Model 5). No significant association existed between widowhood and cognitive
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function across the five main models. In terms of leisure activities, mental activities (Model 1)
and social activities (Model 3) were significantly associated with cognitive function.
Specifically, individuals participating in a higher level of mental activities (b = .236, SE = 0.120,
= .032, p < .05) and social activities (b = .303, SE = 0.120, = .041, p < .05) had significantly
higher scores in cognitive function compared to those who engaged in lower levels of these
activities. However, such main effects disappeared when all four domains of leisure activities
were included simultaneously in the model (Model 5).
Leisure Activities as a Protective Factor
Table 4.4 shows interaction models of multiple regression analyses of the effects of
widowhood and leisure activities on cognitive function. Model 1 presents the main effect of
widowhood status and four domains of leisure engagement on cognitive function (identical to
Model 5 in Table 4.3), whereas Model 2 to Model 5 included interaction terms to test any
moderating effects of each domain of leisure activities on the relationship between widowhood
and cognitive function. Model 2 included mental activities × widowhood, Model 3 included
physical activities × widowhood, Model 4 included social activities × widowhood, and Model 5
included household activities × widowhood interaction terms in addition to all the main variables
included in Model 1. Change in F-statistics were calculated to examine whether each model had
significantly improved by including interaction terms compared to Model 1. As presented in
Table 4.4, the mental activities × transitioned to widowhood interaction term was significant and
there was a significant F-statistics change (p < .05) compared to Model 1. Hence, mental activity
was a significant moderator (b = 1.439, SE = 0.563, = .063, p < .05) in the relationship between
transitioning to widowhood and cognitive function (Model 2).
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To better interpret this interaction, the predicted score of cognitive function by
widowhood and social activity engagement was plotted in Figure 4.2. The three widowhood
groups are depicted in the figure. For the remained widowed group, changing from low to high
levels of social activity engagement was not associated with a significant difference in cognitive
function compared to remained married group, as evidenced by the nonsignificant effect of the
interaction term (Table 4.4, Model 2). However, the transitioned to widowhood group showed a
significant difference in predicted score of cognitive function when mental engagement level
increased from low to high (p < .05; vs. remained married group). In other words, engagement in
mental activity’s effect on cognitive function was more salient for individuals who transitioned
to widowhood than those who remained married, as evidenced by a steeper slope. This pattern
may suggest that mental activity engagement beneficially moderates the relationship between
(transitioning to) widowhood status and cognitive function, partially supporting Hypothesis 4b.
Discussion
The findings of this study contribute to knowledge delineating the mechanisms among
widowhood, leisure activity participation, and cognitive function among older Americans. First,
widowhood had no negative main effect on cognitive function (not supporting Hypothesis 4a);
no significant differences existed in cognitive function at Time 3 between those who remained
married and remained widowed or between those who transitioned to widowhood and who
remained married. Second, the impact of widowhood on cognitive function was moderated by
higher levels of mental activity engagement. This is typically known as a crossover interaction,
i.e., the main effect is not significant but the moderating effect is significant. The results indicate
that the benefit of high-level mental activity engagement was more pronounced among
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individuals who transitioned to widowhood than those who remained married (partially
supporting Hypothesis 4b).
Contrary to the research hypothesis, widowed individuals did not show worse cognitive
function compared to those who remained married. This is inconsistent with several relevant
findings indicating higher risk of cognitive impairment or greater cognitive decline among
widowed individuals compared to their married counterparts (Aartsen et al., 2005; Håkansson et
al., 2009; van Gelder et al., 2006). However, this finding is in line with other studies (Comijs et
al., 2011; Ward et al., 2007). For example, Ward et al. (2007) showed that after controlling for
mood (e.g., depression, anxiety, stress), the negative association between bereavement and
cognitive functioning disappeared. Also, some studies on negative life events have shown similar
findings related to cognitive outcomes among older adults (Comijs et al., 2011; Rosnick, Small,
McEvoy, Borenstein, & Mortimer, 2007). For example, Comijs et al. (2011) found that the death
of a partner was not significantly associated with cognitive decline, but individuals whose
partner experienced illness showed less cognitive decline compared to people without these
experiences. In this vein, nonsignificant widowhood–cognition results may indicate that
widowhood may not have an adverse impact on cognitive function. One speculation regarding
this finding is related to widowed individuals’ previous caregiving experiences before spousal
death. The relief hypothesis suggests that being relieved from prebereavement caregiving strain
may provide stress relief for surviving partners and thereby enhance health outcomes (Li, 2005;
Schulz et al., 2001). Indeed, Vable, Subramanian, Rist, and Glymour’s (2015) widowhood study
showed that compared to continuously married individuals, near widows (those before 2 years of
widowhood) reported more functional limitations, depressive symptoms, and worse word recall.
These authors further discussed that such prewidowhood effects on health outcomes may be
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attributable to poor health management (e.g., health checkups, medication refills), caregiving
burden, and anticipatory grief before widowhood. Thus, better self-care or removed caregiving
strain after spousal death may provide some form of relief to widowed individuals from previous
caregiving burden, which may offset possible negative widowhood influences (if any exist) on
cognitive function. Nevertheless, to assess this speculation, future studies should examine
caregiving experiences among widowed individuals to determine whether previous caregiving
plays any significant role in cognitive function.
In terms of the main effects of leisure activities, none of the domains was significant
when included simultaneously in the model (Table 4.3, Model 5). However, when each domain
was included independently, mental and social activities showed a significant positive
association with cognitive function (Table 4.3, Model 1 and Model 3). Although no
multicollinearity issue emerged while conducting regression analyses, controlling for other
domains of leisure activities may have resulted in null findings for individual leisure domains.
Future studies will be necessary to specifically examine independent and joint effects of various
domains of leisure activities on cognitive function.
It is noteworthy that this study further found a moderating effect of mental activity
engagement on the relationship between widowhood and cognitive function. Compared to
individuals who remained married, those who transitioned to widowhood benefited more by
actively engaging in mental activities (e.g., reading books, playing cards or games, solving
puzzles, doing arts and crafts, listening to music, singing for playing a musical instrument,
praying or meditating) in terms of cognition. This might suggest that compensating for spousal
loss with mentally stimulating activities can protect cognitive function among older adults,
especially in the early phase of transitioning to widowhood. As Patterson (1996) discussed in his
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qualitative study of older adults who experienced the loss of a spouse, early stages of the
grieving process may restrict widowed individuals from participating in leisure activities due to
lack of incentives or motivation after a significant loss. He further mentioned that “many felt a
lack of purpose and direction in their lives in the first two years after the loss” (p. 139). Hence,
encouraging mental activities among this population after bereavement are necessary at the
community level (e.g., adult day health centers).
Nevertheless, Utz et al. (2002) argued (after finding that widowed individuals
participated more in informal social participation than married individuals) that simply providing
new activity programs (e.g., at senior centers) may not be the most effective way to support the
widowed population, because many older adults do not take advantage of these formal leisure
activities as much as they rely on lifelong social relations (e.g., family members, friends,
religious communities). In this sense, familiar sources of social support may become the bridge
for widowed individuals’ motivation to participate in leisure pursuits, including mental activities.
Unfortunately, although adult children may be the closest ties for older individuals, many older
adults are reluctant to rely on their adult children to avoid imposing a heavy burden on them
(Balkwell, 1981). Thus, intervention efforts will also be necessary at the policy level to support
family caregivers or religious community members who are primarily connected with these
widowed individuals to encourage leisure engagement.
Last, other activities such as physical, social, and household activities were not
significant moderators in the widowhood–cognition relationship. To test further whether leisure
activities protect cognition as a process, the mediating roles of four domains of leisure activities
were also tested (results not shown, available upon request), but no significant indirect path from
widowhood to cognitive function emerged for any domain.
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Limitations
Limitations of this study should be noted and addressed in future research. First,
individuals categorized as transitioned to widowhood were those who reported widowhood at
either Time 2 or Time 3, which may have led to heterogeneity in this group due to different
widowhood periods, ranging from several months to a maximum of 4 years. Due to small sample
size (n = 122) for this group, those widowed at Time 2 and Time 3 had to be combined; future
studies separating this group will better explain study findings. Second, the classification of
leisure domains was not clear enough, given that certain items can be included in multiple
domains. For example, attending concerts, movies, or lectures, or visiting museums can be
considered a social and mental activity. Classification of leisure domains can be very subjective
and may be perceived differently from person to person depending on the context of leisure
pursuits. The CAMS questionnaire does not ask about specific context (e.g., leisure partner,
purpose of leisure participation; Morrow-Howell et al., 2014), so it was difficult to understand
the explicit motivation for leisure engagement. Thus, future studies further incorporating the
leisure context and testing how to measure leisure domains in a more consistent and valid way
are necessary.
Conclusions
Transitioning to widowhood in later life may seem more common and less traumatic than
experiencing such an event in midlife. Nevertheless, losing a spouse may significantly affect
older individuals, who are left alone without their most important companion while additionally
facing challenges related to aging. In this vein, the protective role of mental activities among
individuals who transitioned to widowhood as found in this study suggests that compensating
spousal loss through mental stimulation may serve as a coping strategy to maintain cognitive
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function and promote better adjustment to postwidowhood life in this population. Interventions
at the community and policy levels that encourage engagement in mentally stimulating activities
among recently widowed individuals are recommended.
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Table 4.1. Subdomains of Leisure Activities from the CAMS
Mental (7 Items) Physical (2 Items) Social (9 Items) Household (8 Items)
Reading newspapers or
magazines
Reading books
Playing cards or games, or
solving puzzles
Doing arts and craft projects,
including knitting, embroidery,
or painting
Listening to music
Singing or playing a musical
instrument
Praying or meditating
Walking
Participating in sports or other
exercise activities
Visiting in person with friends,
neighbors, or relatives
Communicating by telephone,
letters, or email with friends,
neighbors, or relatives
Helping friends, neighbors, or
relatives
Physically showing affection
for others through hugging,
kissing, etc.
Doing volunteer work for
religious, educational, health-
related, or other charitable
organizations
House cleaning
Preparing meals and cleaning
up afterward
Washing, ironing, or mending
clothes
Shopping or running errands
Home improvements,
including painting,
redecorating, or making home
repairs
Yard work or gardening
Working on, maintaining, or
cleaning a car or vehicle
Caring for pets
103
Attending religious services
Attending meetings of clubs or
religious groups
Attending concerts, movies, or
lectures, or visiting museums
Dining or eating outside the
home (not related to business
or work)
Note. CAMS, Consumption and Activities Mail Survey; 7 items (e.g., “watching television,” “sleeping and napping,” “grooming and hygiene,” “using
computer,” “working for pay,” “taking care of finances or investments, such as banking, paying bills, balancing the checkbook, doing taxes,” and “self-treating or
self-managing an existing medical condition”) were excluded from this study.
104
Table 4.2. Sample Characteristics by Widowhood Status (N = 2,618)
Married
a
Transition
b
Widowed
c
Married vs.
Transition
d
Married vs.
Widowed
d
(n = 2,072) (n = 122) (n = 424)
Range n (%) or M (SD) n (%) or M (SD) n (%) or M (SD) p p
Age (years) 51–93 64.64 (8.06) 68.89 (8.36) 72.73 (9.06) < .001 < .001
Gender < .001 < .001
Female 1,066 (51.45) 91 (74.59) 376 (88.68)
Male 1,006 (48.55) 31 (25.41) 48 (11.32)
Race and ethnicity 1.000 1.000
White 1,838 (88.71) 107 (87.70) 358 (84.43)
Black 107 (5.16) 9 (7.38) 45 (10.61)
Hispanic 98 (4.73) 4 (3.28) 17 (4.01)
Other 29 (1.40) 2 (1.64) 4 (0.94)
Retirement status < .01 < .001
Working 975 (47.06) 38 (31.15) 102 (24.06)
105
Retired 1,097 (52.94) 84 (68.85) 322 (75.94)
Education 0–17 13.47 (2.60) 12.69 (2.37) 12.52 (2.37) < .01 < .001
Household wealth ($1,000) -100.81–
26,040
598.68 (1,105.05) 421.81 (841.25) 285.35 (580.55) .194 < .001
Depression 0–8 0.82 (1.46) 1.31 (1.78) 1.53 (1.90) < .01 < .001
Functional limitation
1.000 < .01
None 1,988 (95.95) 117 (95.90) 389(91.75)
Any 84(4.05) 5(4.10) 35(8.25)
Drinking status 1.000 < .001
Never 816 (39.38) 56 (45.90) 233 (54.95)
Light 984 (47.49) 49 (40.16) 142 (33.49)
Heavy 272 (13.13) 17 (13.93) 49 (11.56)
Smoking status .975 1.000
Never 889 (42.91) 49 (40.16) 202 (47.64)
Former 964 (46.53) 56 (45.90) 164 (38.68)
Current 219 (10.57) 17 (13.93) 58 (13.68)
106
Presence of living children .1000 < .01
None 63 (3.04) 3 (2.46) 27 (6.37)
One or more 2,009 (96.96) 119 (97.54) 397 (93.63)
Leisure engagement, Time 2
Mental activity .193 < .001
High engagement 994 (47.97) 69 (56.56) 255 (60.14)
Physical activity 1.000 < .001
High engagement 988 (47.68) 56 (45.90) 153 (36.08)
Social activity 1.000 1.000
High engagement 1,030 (49.71) 66 (54.10) 221 (52.12)
Household activity 1.000 < .05
High engagement 1,059 (51.11) 66 (54.10) 188 (44.34)
Cognition, Time 3 0–27 16.48 (3.57) 16.17 (4.15) 15.44 (3.88) 1.000 < .001
Cognition, Time 1 12–27 17.32 (2.92) 16.73 (2.87) 16.65 (3.04) .096 < .001
a
Remained married.
b
Transitioned to widowhood.
c
Remained widowed.
d
P-values for chi-square and analysis of variance tests.
107
Table 4.3. Multiple Regression of Widowhood and Leisure Activities on Cognitive Function
All models are controlled for age, age
2
, male, Black, Hispanic, other, education, self-rated health, depressive symptoms, functional limitation, household wealth
(log+1), smoking behavior, drinking behavior, retirement status, presence of a living child, and cognitive function at baseline.
*p < .05. **p <. 01. ***p < .001.
Model 1 Model 2 Model 3 Model 4 Model 5
b SE β b SE β b SE β b SE β b SE β
Widowhood status
Transition .406 .283 .023 .410 .283 .024 .408 .283 .023 .421 .283 .024 .410 .283 .024
Widowhood .207 .187 .021 .213 .187 .021 .216 .187 .022 .243 .188 .024 .233 .188 .023
Leisure activities
Mental .236* .120 .032 .161 .125 .022
Physical .080 .120 .011 -.005 .123 -.001
Social .303* .120 .041 .244 .125 .033
Household .201 .123 .027 .135 .126 .018
R
2
.3471 .3463 .3477 .3468 .3485
108
Table 4.4. Interaction Models of Widowhood and Leisure Activities on Cognitive Function
Model 1 Model 2 Model 3 Model 4 Model 5
b SE β b SE β b SE β b SE β b SE β
Widowhood status
Transition .410 .283 .024 -.399 .425 -.023 .314 .382 .018 -.057 .413 -.003 .012 .415 .001
Widowhood .233 .188 .023 .010 .267 .001 .463* .227 .047 .418 .253 .042 .087 .247 .009
Leisure activities
Mental .161 .125 .022 .035 .138 .005 .162 .125 .022 .160 .125 .022 .155 .125 .021
Physical -.005 .123 -.001 -.009 .123 -.001 .073 .136 .010 -.005 .123 -.001 -.002 .123 -.0003
Social .244 .125 .033 .240 .125 .033 .247* .125 .034 .260 .139 .036 .247* .125 .034
Household .135 .126 .018 .133 .126 .018 .140 .126 .019 .137 .126 .019 .050 .142 .007
Interaction terms
Transition × mental 1.439* .563 .063
Widowhood × mental .381 .324 .031
Transition × physical .215 .556 .008
Widowhood × physical -.598 .329 -.038
Transition × social .866 .558 .037
Widowhood × social -.350 .319 -.027
Transition × household .725 .559 .031
Widowhood × household .285 .323 .020
R
2
.3485 .3504 .3494 .3495 .3491
∆R
2a
.0019* .0009 .0010 .0006
109
Note. Model 1: main effects model, Model 2: main effects and mental activity × widowhood status interaction model, Model 3: main effects and physical activity
× widowhood status interaction model, Model 4: main effects and social activity × widowhood status interaction model, Model 5: main effects and household
activity × widowhood status interaction model. All models are controlled for age, age
2
, male, Black, Hispanic, other, education, self-rated health, depressive
symptoms, functional limitation, household wealth (log+1), smoking behavior, drinking behavior, retirement status, presence of a living child, and cognitive
function at baseline.
a
R
2
change compared to Model 1.
*p < .05. **p <. 01. ***p < .001.
110
Figure 4.1. Sample Selection Criteria
Note. Wave 1: HRS 2004 and CAMS 2005; Wave 2: HRS 2006 and CAMS 2007, Wave 3: HRS 2008 and CAMS
2009.
Wave 1, 2, 3
N = 4,175
n = 3,942
n = 3,290
n = 2,898
Excluded individuals with
cognitive impairment (score <12)
at baseline: n = 392
Excluded individuals missing
more than one item in each
leisure activity domain: n = 88
n = 2,810
n = 2,618
Excluding missing cases in the
study variables: n = 192
Excluded individuals younger
than 51 at baseline: n = 233
Excluded individuals who were
separated, divorced, or never
married: n = 652
111
Figure 4.2. Mental Activity by Widowhood Status Interactions on Cognitive Function
112
Chapter 5: Conclusion
Maintaining cognitive function is one of the crucial aspects of successful aging (Rowe &
Kahn, 1997). Thus, delineating risk and protective factors of cognitive function has received
increasing attention in recent years due to prolonged longevity and the growing number of older
adults in the United States. According to cognitive reserve theory (Scarmeas & Stern, 2003; Y.
Stern, 2002), life experiences such as educational attainment, occupational complexity, and later-
life leisure activity engagement may expand cognitive reserve, which enables individuals to
become resilient to brain damage, thus manifesting fewer cognitive deficits than their
counterparts with less cognitive reserve. Among these factors, however, leisure activity
engagement has been examined in greater detail during the last two decades in cognition studies,
because it is still modifiable in later life. Nevertheless, few longitudinal studies have specifically
examined later-life predictors of change in leisure activity participation.
Life transitions, such as retirement and loss of a spouse, are among the most salient
events a majority of older adults experience in later life, which may influence cognitive function.
Several studies have examined the impact of these life transitions on cognitive function but have
presented mixed results (Aartsen et al., 2005; Adam et al., 2013; Bonsang et al., 2012; Coe &
Zamarro, 2011; Comijs et al., 2011; Håkansson et al., 2009; Helmer et al., 1999; Rohwedder &
Willis, 2010; Sundström et al., 2014; van Gelder et al., 2006), leaving the question of whether
any third factor plays a role in this life transition and cognition relationship. Leisure activities
have often been noted as a coping resource that protects against negative health outcomes among
older adults who experience significant life events (Iso-Ahola et al., 1994; Iwasaki, 2003). In this
respect, leisure engagement may play crucial role in the relationship between life transitions and
cognition to promote better adjustment following retirement or widowhood. However, previous
113
studies have rarely examined the role of leisure activities specifically in the context of the life
transition and cognition relationship. To address this gap, this dissertation examined how leisure
activity engagement changes over time as influenced by retirement and widowhood (Study 1)
and further examined the role of leisure activities in the relationship between retirement and
cognitive function (Study 2) and between widowhood and cognitive function (Study 3).
Summary of Research Findings
The first study provided an understanding of how leisure activity engagement changes
over time among older adults. The findings show that leisure participation significantly
decreased over time across all four domains of activities—mental, physical, social, and
household. Moreover, the study found that leisure activity participation was significantly
influenced by retirement and widowhood status above and beyond covariates, including
sociodemographic and health-related factors.
In terms of retirement, participation in mental, social, and household activities increased
when older individuals transitioned from working to retirement status. This may imply that
retired individuals use leisure activities to compensate for their lost social roles and time
previously dedicated to work. However, physical activities were not significantly associated with
retirement in this study, which is not consistent with several previous studies (Evenson et al.,
2002; Janke et al., 2006) showing increased physical activity after retirement. This may result
from the offset of increased physical activities during leisure time (e.g., playing sports, walking
for leisure) by decreased physical activities at work (e.g., walking while commuting to or
engaging in work). Nevertheless, items measuring physical activities in this study did not
specifically assess the purpose of engagement (e.g., for work or fun). Future studies
114
incorporating this information will be necessary (Morrow-Howell et al., 2014) to provide a better
understanding of leisure behaviors among retired populations.
With regard to widowhood, the study found that participation in household activities
decreased when older individuals transitioned from married to widowhood status. This may
imply that the workload of household activities diminished due to the absence of a previous
family member at home or that widowed individuals are no longer motivated to engage in
household activities previously shared with their partner. However, intergenerational support
may also come into play after widowhood if adult children increase their support (e.g., assisting
with household chores) of widowed parents as a filial norm (Silverstein et al., 2006). Thus,
future studies considering intergenerational factors to examine whether they influence household
activities in the context of widowhood will be necessary. Moreover, because household activities
may be largely influenced by gender roles (South & Spitze, 1994; Utz, Reidy, Carr, Nesse, &
Wortman, 2004), further studies that account for the gender effect are needed.
The second study examined the role of leisure activities in the relationship between
retirement and cognitive function. First, findings show that compared to individuals who
remained working, those who remained retired showed poorer cognitive function, which may
support the “use it or lose it” hypothesis. This negative association between retirement and
cognitive function was partially mediated by mental activity engagement (e.g., reading books,
solving puzzles, doing arts and crafts). Specifically, engagement in higher levels of mental
activities attenuated the negative impact of retirement on cognitive function. However, other
domains of activities had no direct or indirect effects on cognition. This is in line with more
consistent findings of the benefit of mental activities in terms of cognitive function compared to
other activities, including physical and social activities (Verghese et al., 2003; J. Y. J. Wang et
115
al., 2006; Wilson et al., 2002). This finding further suggests the need for interventions that
encourage mental activities among retired individuals by promoting mentally stimulating
programs in senior centers or retirement communities to help them maintain cognitive function.
The third study examined the impact of widowhood on cognitive function and further
explored the coping role of leisure activities in this relationship. The study findings show no
significant association between widowhood and cognitive function. This result may indicate that
widowhood may not necessarily have an adverse impact on cognitive function. This supports
some previous studies showing no significant relationship between widowhood and cognitive
function (Comijs et al., 2011; Rosnick et al., 2007). One explanation may be related to
caregiving experiences before widowhood. Surviving spouses may be relieved from a caregiving
burden and have better opportunities to take care of their own health (e.g., health checkups,
refills of medication) postwidowhood, which may offset any possible negative widowhood effect
on cognitive function. The study also found a moderating effect of mental activity engagement
on the relationship between widowhood and cognitive function. Specifically, individuals who
transitioned to widowhood cognitively benefited more from higher levels of mental activity
participation than those who remained married. This suggests that compensating for spousal loss
with mentally stimulating activities may protect cognitive function among widowed older adults,
especially in the early phase of transitioning to widowhood.
This study contributed to previous research by investigating changes in leisure activity
engagement from a longitudinal perspective. This enhanced our understanding not only of how
older Americans change their time spent in leisure activities over time but also how such changes
in specific domains of leisure activities are influenced by retirement and loss of a spouse.
Moreover, the present study strived to better understand the mechanism among life transitions,
116
leisure engagement, and cognitive function. Mixed results from previous findings regarding the
life transitions–cognition relationship have been to some extent explained by the protective role
of mental activity engagement found in this study.
Implications for Research
As mentioned in a systematic review by C. Stern and Munn (2010), one of the challenges
of comparing leisure studies is their different methods of measurement (e.g., frequency,
intensity, variability) and lack of consistency in grouping (e.g., same item classified into
different domains), which makes it difficult to compare results across relevant studies. Thus,
future studies specifically targeting how to improve the valid classification of leisure domains
are necessary. Moreover, delineating which specific activities even within the mental leisure
activities domain have a greater impact on cognition is necessary in the context of older adults
who experience retirement, widowhood, or both. For example, some mental activities may be
more beneficial to retired individuals (e.g., mental activities previously performed at work such
as sending emails or using computer) or widowed individuals (e.g., mental activities to establish
rapport with new leisure companions) compared to older adults in general.
Moreover, the mechanisms found in this study among life transitions, leisure activities,
and cognitive function can be further tested with other crucial factors. For example, the
magnitude of the mediating effect of mental activities in the relationship between retirement and
cognitive function may vary depending on older individuals’ socioeconomic status (e.g., wealth,
education) and physical and mental health (e.g., chronic disease, life satisfaction). Thus,
examining possible socioeconomic or health-related factors as moderators in this mediation
model may provide a clearer understanding of how this mechanism works for individuals based
on distinguishable social and health statuses. Likewise, the study result regarding the moderating
117
role of mental activities in the relationship between widowhood and cognitive function suggests
further exploration of physical and mental health (e.g., depression) or intergenerational support
(e.g., instrumental support from adult children) following widowhood is needed.
Implications for Social Work Practice
The study findings show that ceasing work may lead to withdrawal from mental
stimulation (e.g., use it or lose it; Hultsch et al., 1999) after retirement, which may in turn
negatively affect cognitive function among retired individuals. The significant mediating effect
of mental activity engagement suggests that promoting mentally stimulating activities among
retired individuals might attenuate cognitive decline after retirement. Likewise, the significant
moderating effect of mental activity engagement among individuals who transitioned to
widowhood (vs. those who remained married) suggests that encouraging mentally stimulating
activities in this population will benefit their cognitive function, which may in turn help them
better readjust to postwidowhood life. In this respect, promoting mental activities (e.g., playing
board games, doing arts and crafts, reading books, listening to music) is suggested in community
settings, including senior day health centers and retirement communities.
Moreover, mentally stimulating activities using technology can be a useful tool to access
and engage current older adults, who are relatively familiar with using technology in daily life
(e.g., computer, smartphones, internet) and more proficient with computer skills due to their
previous career or jobs (Gatto & Tak, 2008) compared to prior generations. Technology also has
significant advantages for homebound older retirees or widowed individuals who are physically
unable to attend various activity programs provided at senior centers or other venues. Employing
interactive technology will not only prevent older adults from becoming socially isolated but also
provide more opportunities to become mentally active, thereby improve their quality of life (e.g.,
118
connecting with friends and families, engaging in self-health care and learning experiences,
performing daily tasks such as banking and shopping; Czaja et al., 2006; Gatto & Tak, 2008).
However, adoption of technology (e.g., computer, internet) may be more challenging for
individuals at lower socioeconomic levels, including older people with less education and wealth
(Gatto & Tak, 2008). Developing interventions that encourage access to and participation in
technology-based mental activities at an affordable cost for physically or socially disadvantaged
older adults who experience life transitions will be necessary.
119
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Abstract (if available)
Abstract
Maintaining cognitive function is an essential aspect of successful aging because it enables older adults to retain autonomy and a sense of self into late life. Retirement and widowhood are the two salient life transitions that can affect older adults’ health, including cognitive function. However, limited longitudinal studies have examined how retirement and widowhood influence cognitive function and few of these studies showed equivocal results, leaving the question of whether any third factor plays a role in this life transitions–cognition relationship. In another line of inquiry, leisure engagement is receiving increasing attention because it is still modifiable in later life to help prevent cognitive decline. Moreover, it has been considered to serve as an adjusting and coping resource relative to various health outcomes (e.g., functional limitations, life satisfaction) among individuals who experience significant life events. Nevertheless, little is known about how leisure activity specifically affects cognitive outcomes influenced by retirement and loss of a spouse. Thus, this dissertation delineated the mechanism of life transitions, leisure activity engagement, and cognitive function among older adults using a national longitudinal data, the Health and Retirement Study (HRS), and its supplementary data, the Consumption and Activities Mail Survey (CAMS), which repeatedly measured individuals’ leisure activity engagement. Leisure activities were classified into four domains (mental, physical, social, and household activities) to investigate how specific domains of leisure activities change over time and influence cognitive function to a distinguishable degree. ❧ The dissertation is organized into three papers. The first paper explored the trajectory of leisure activity engagement as influenced by retirement and widowhood among older adults. The second and the third papers investigated the impact of retirement and widowhood on cognitive function and the role of leisure activity engagement in this relationship. The first study showed that leisure activity engagement significantly decreased during an 8-year period across all four domains of activities. Transitioning from working to retirement status significantly increased engagement in mental, social, and household activities, whereas transitioning from married to widowhood status significantly decreased engagement in household activities. The second study showed that retirement was negatively associated with cognitive function during a 4-year period. Specifically, individuals who remained retired showed a significantly lower level of cognitive functioning than those who remained working. Moreover, engagement in mental activities mediated this negative relationship between retirement and cognitive function. In particular, the negative impact of retirement on cognitive function was attenuated by higher levels of mental activity engagement. The third study showed no significant association between widowhood and cognitive function during a 4-year period. However, engagement in mental activities moderated the impact of widowhood on cognitive function. Specifically, the benefit of mental activity engagement on cognition was more pronounced among individuals who transitioned to widowhood compared to those who remained married. ❧ The findings of this dissertation indicated the protective role of mental activities in the relationship between retirement or widowhood and cognitive function. This suggests the need for increased interventions with mentally stimulating activities at the community level (e.g., in senior centers) to retain cognition among retirees and individuals in early phase widowhood. Future studies should investigate whether other factors such as socioeconomic status, physical and mental health, and intergenerational support from adult children after retirement or widowhood may play a significant role to further influence the mechanism among life transitions, leisure activities, and cognitive function.
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Creator
Lee, Yura
(author)
Core Title
Life transitions, leisure activity engagement, and cognition among older adults
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School of Social Work
Degree
Doctor of Philosophy
Degree Program
Social Work
Publication Date
07/20/2019
Defense Date
05/04/2017
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
cognition,Leisure activities,life transitions,OAI-PMH Harvest,older adults,Retirement,widowhood
Format
application/pdf
(imt)
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Chi, Iris (
committee chair
), Ailshire, Jennifer (
committee member
), Gatz, Margaret (
committee member
), Palinkas, Lawrence (
committee member
)
Creator Email
yuracaralee@gmail.com,yuralee@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-c40-412668
Unique identifier
UC11214635
Identifier
etd-LeeYura-5604.pdf (filename),usctheses-c40-412668 (legacy record id)
Legacy Identifier
etd-LeeYura-5604.pdf
Dmrecord
412668
Document Type
Dissertation
Format
application/pdf (imt)
Rights
Lee, Yura
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
Tags
cognition
life transitions
older adults
widowhood