The influence of social relationships and
leisure activity on adult cognitive
functioning and risk of dementia
Longitudinal Population-based Studies
Daniel Eriksson Sörman
Department of Psychology
Umeå 2015
This work is protected by the Swedish Copyright Legislation (Act 1960:729)
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”Pappa. Du lyssnar aldrig och minns ingenting. Du borde forska på ditt
eget minne”
- Jonathan, 2015-01-19
Acknowledgements
The past few years have been stimulating and exciting for me, and I have
been given the opportunity to study in a pleasant working environment, for
which I am very grateful. Although being a PhD student is not entirely
associated with positive feelings, at least not closer to the end, such negative
memories will most likely fade after I have crossed the finish line.
I would like to take the opportunity to thank several people who have
helped me through these past few years. And even though forgetting is
natural, I do apologize if I forget to mention some of those who have played
an important role in this project.
First and foremost, I would like to give my deepest gratitude to my
supervisors Michael Rönnlund, Anna Sundström, and Lars-Göran Nilsson.
Your insightful comments, methodological knowledge, and just keeping me
on track during these years is greatly valued. Thank you for being supportive.
Further, thank you Michael Rönnlund for giving me confidence in the
bowling alley, and Anna Sundström for your deep understanding about what
a loss in a football game can do to one’s mood. You make a great team! LarsGöran Nilsson, thank you for giving me the opportunity to be a PhD student,
and, of course, to work with a fantastic database.
Thanks to all at the Department of Psychology and to those who have
worked within the Betula study in previous years. I am grateful to have had
so many fine colleagues. A special thanks to Maud, you are great. To all
participants in Betula, without you there would be no thesis.
I am also very grateful to the Graduate School in Population Dynamics
and Public Policy, and director Johan Lundberg, who over the years has
given me the opportunity for international exchange, conference
participation, and many interesting interdisciplinary workshops. This will be
of great benefit to me in the future.
Annika Nordlund and Carola Wiklund-Hörnqvist, thank you for giving
valuable feedback on an earlier draft of this thesis. I hope that you will enjoy
the final version. Bo Molander and Peter Hassmén, thank you for your
valuable comments on the mid-seminar.
Bo Molander, you have been a great support and friend since my
arrival at Umeå University in 2001 (although the first meeting was a shock).
Without you, I would most certainly not have been a PhD-student. Thank
you for giving me my current interest for research. Eternally grateful. Carola
and Patrik, since 2008, we have followed each other and shared many
interesting discussions, to say the least. I have very much appreciated these
chats. I enjoy both of your personalities.
I am also thankful that I have made many friends within the PhD
group. Thank you Susanne, Inga, Nina, Erik M, Erik L, Erika S, Erica E, Eva
P, Johan, Robert, Helen, Hanna, Stefan B, Anna-Maria, Carola, Petra, Linus,
1
Ingrid, Stenling, Esther, Elisabeth, Olov, Olympia, Marius, and Markus for
these years (hope I haven't missed anyone). The atmosphere within the PhD
group has been very good. I hope this continues. Anna-Maria, you're a very
kind person and I really like your strong belief that all people can achieve
their goals if they work hard enough. Petra, I enjoyed being an internal
reviewer at your final seminar. Good luck with far transfer, I keep my fingers
crossed. Esther, it was great fun in Barcelona, a real highlight. Stenling,
thank you for working for a healthy environment for our sports-active
children. Marius, please teach me how to act. Linus, intolerance is an
important aspect also for a king. Erik L, why a playoff beard when you never
experience it? Olov, there can be only one, let it grow. Markus, let's go fishing
instead of “always walking alone”. Elisabeth, I’ve tried to be a good mentor,
but I admit that being frank was not always easy. Eva P, Kelly McGillis says
“Hi”. I'll save the rest for later. All my respect to you guys.
Stefan H. & Ulrich, what about Patrick Thistle? By the way, Stefan,
you are a good sport, looking forward to theory and practice. Gelfgren,
competitions are fun, and even more fun if everyone is given the chance to
win. Markus, Jessica, and Erik M, LA was a great experience. Maybe "Sweet
Home Chicago" next time? We’ll see.
Family and friends, I know that I have been remiss in keeping in
touch. Forgive me for my physical and mental absence. I will improve. Just
know that I appreciate you all very much. Linda, my wife, thank you for all
your encouragement and endless support. Love you. My sons, William and
Jonathan, you are most important to me. Please don’t forget.
Umeå, April 2014
2
Table of Contents
1
Acknowledgements
Table of Contents
3
Abstract
5
List of papers
7
8
Sammanfattning
10
Introduction
The basis of individual differences in cognitive functioning in old age
11
Models of cognitive aging
13
The cognitive reserve hypothesis
15
Effects of cognitive stimulation on the brain
16
Effects of leisure activity
16
Other pathways from leisure activity to cognitive functioning
17
Reverse causality and other factors related to patterns of leisure activity 18
19
Effects of social relationships
Other pathways from social relationships to cognitive functioning
20
Reverse causality and other factors related to patterns of social relations 21
22
Memory systems
Declarative and nondeclarative memory
23
Episodic and semantic memory
23
Episodic and semantic memory functions in normal aging
24
25
Working memory
Visuospatial ability
26
Visuospatial ability in normal aging
26
Differentiating dementia progress from normal aging
26
Dementia disorders
28
All-cause dementia
28
Alzheimer’s disease
29
Vascular dementia
30
Other dementias
31
Additional factors associated with cognitive functioning and risk of dementia
32
Demographic factors
32
Health
34
Lifestyle
37
39
Genetics
40
The empirical studies
40
Main objectives
40
Specific aims:
41
Methods
The Betula Prospective Cohort Study
41
The Västerbotten Intervention Programme
42
3
The Linnaeus Database
42
Measures
43
49
Summary of Study I
49
Aim
Participants
49
Statistical procedure
50
Results
51
53
Summary of Study II
53
Aim
Participants
53
Statistical procedure
53
Results
54
57
Summary of Study III
57
Aim
Participants
57
Statistical procedure
58
59
Results
60
General discussion
Results in relation to the cognitive reserve hypothesis
61
Support for an active or passive model?
63
What is required to affect the cognitive reserve?
64
Effects might differ depending on outcome
65
Cognitive reserve or reverse causation?
65
Strengths
66
Limitations
67
Implications and future directions
69
Conclusions
71
72
References
Study l
Study ll
Study lll
4
Abstract
Today, as we live longer, dementia diseases are becoming more prevalent
around the world. Thus, further knowledge of how to maintain levels of
cognitive functioning in old age and how to identify factors that postpone the
onset of dementia are of acute interest. Lifestyle patterns and social life are
important aspects to consider in this regard.
This thesis includes three studies. In Studies I and II, data from the
Betula prospective cohort study were examined. Betula is a population-based
longitudinal project that started in Umeå, Sweden, in 1988. Study I
investigated the association between participation in various leisure
activities in old age (•65 years) and risk of incident all-cause dementia.
Analyses of the total follow-up time period (15 years) showed that higher
levels of “Social” and “Total” leisure activity were associated with decreased
risk of dementia. In Study II, the aim was to investigate the association
between various aspects of social relationships in old age (•65 years) and
risk of incidents of all-cause dementia and Alzheimer's disease. Results
showed that over the total follow-up period (16 years) higher values on the
relationship index were associated with reduced risk of both dementia and
Alzheimer's disease. Visiting/visits of friends and acquaintances more than
once a week was related to decreased risk for all-cause dementia, but not for
Alzheimer's disease. However, in neither Study I nor II did any of these
factors alter the risk of all-cause dementia or Alzheimer's disease when nearonset dementias were removed from the analyses (Study I, up to five years;
Study II, up to three years).
Study III included participants who had taken part both in the Betula
study and the Västerbotten Intervention Programme. The aim was to
investigate the association between social network size and cognitive ability
in a middle-aged (40–60 years) sample. The idea was that if social network
size can moderate negative age-related influence on memory functions, it
might also put an individual on a cognitive trajectory that is beneficial in old
age. Results from longitudinal analyses showed that baseline network size
was positively related to five-year changes in semantic memory and with
changes in both semantic and episodic memory at the ten-year follow-up.
Social network size was unrelated to changes in visuospatial performance.
Taken together, enrichment factors measured in old age (• 65 years)
did not alter the risk of all-cause dementia or Alzheimer's disease when nearonset dementias were removed from the analyses. These results might reflect
protective short-term effects or reverse causality, meaning that in the
prodromal phase of dementia individuals tend to withdraw from activity.
Social network size in middle age (40-60 years), however, appears to have
beneficial long-term effects on cognitive functioning. The results highlight
the importance of long follow-up periods and the need to adjust for the
5
influences of reverse causality when investigating the impact of a socially and
mentally active life on cognitive functioning.
6
List of papers
I.
Sörman, D. E., Sundström, A., Rönnlund, M., Adolfsson, R., &
Nilsson, L.-G. (2014). Leisure activity in old age and risk of
dementia: a 15-year prospective study. Journals of Gerontology,
Series B: Psychological Sciences and Social Sciences, 69, 493–501.
II.
Sörman, D. E., Rönnlund, M., Sundström, A., Adolfsson, R., &
Nilsson, L.-G. (2015). Social relationships and risk of dementia: a
population-based study. International Psychogeriatrics. Advance
online publication.
III.
Sörman, D. E., Rönnlund, M., Sundström, A., Norberg, M., &
Nilsson, L.-G. (2015). Social network size and cognitive functioning
in middle-aged adults: Cross-sectional and longitudinal associations.
Manuscript.
7
Sammanfattning
Som en konsekvens av att vi lever längre blir demenssjukdomar allt vanligare
runt om i världen. Ökad kunskap om hur vi bibehåller god kognitiv funktion
i åldrandet samt identifierar faktorer som kan skjuta upp tidpunkten för
insjuknande i demenssjukdomar är därför av stor vikt. Livsstilsmönster och
socialt liv är viktiga aspekter att beakta i sammanhanget.
Denna avhandling omfattar tre studier. I Studie I och II analyserades
information som samlats in inom ramen för Betulaprojektet, som är en
populationsbaserad longitudinell studie som startade i Umeå 1988. Studie I
undersökte sambandet mellan engagemang i olika fritidsaktiviteter i hög
ålder (•65 år) och risk för demens. Resultaten visade, om man tog hela
uppföljningsperioden i beaktning (15 år), att högre nivåer både på ett
"socialt" och "totalt" fritidsaktivitetsindex var associerat med minskad risk
för demens. I Studie II var syftet att undersöka hur olika aspekter av sociala
relationer i hög ålder (•65 år) är relaterade till risk för demens och
Alzheimers sjukdom. Resultaten visade att högre värden på ett
relationsindex var associerade med minskad risk både för demens generellt
och Alzheimers sjukdom specifikt när den totala uppföljningsperioden (16
år) inkluderades i analyserna. Att besöka eller att få besök av vänner och
bekanta mer än en gång i veckan var också associerat med reducerad risk för
demens, men inte för Alzheimers sjukdom. Det bör dock understrykas att
ingen av faktorerna var statistiskt signifikanta, vare sig i Studie I eller II, när
de personer som insjuknad strax efter baslinjemätningen av fritidsaktivitet
och sociala relationer exkluderats från analyserna (Studie I, upp till fem år
efter; Studie II, upp till tre år efter)
I Studie III inkluderades individer som deltagit både i Betulaprojektet
och i Västerbottens hälsoundersökning. Data från båda studierna finns
länkad i Linnédatabasen. Syftet var att undersöka sambandet mellan storlek
på socialt nätverk och kognitiv förmåga i en medelålders (40-60 år)
population. Tanken var att om nätverksstorlek kan modifiera negativa
åldersrelaterade kognitiva förändringar hos denna åldersgrupp, kan det
potentiellt också ha en långvarig effekt som blir gynnsam i hög ålder.
Resultaten visade att större socialt nätverk vid baslinjemätning var positivt
relaterat till förändring i semantiskt minne över fem år. Över tio år var det
relaterat till förändring både i semantiskt och episodiskt minne. Storleken på
det sociala nätverket hade inte något longitudinellt samband med
visuospatial förmåga.
Sammantaget visar resultaten att faktorer som undersökts vid hög
ålder (• 65 år) inte har något samband med demens eller Alzheimers
sjukdom då de som insjuknat strax efter baslinjemätning exkluderats från
analyserna. Detta kan eventuellt indikera skyddande korttidseffekter, men
det kan också spegla omvänd kausalitet, det vill säga att personer i ett
8
förstadium till demens blir mindre aktiva. Storleken på det sociala nätverket
i medelåldern (40-60 år) tycks emellertid kunna ha långsiktiga effekter på
vissa kognitiva förmågor. Resultaten belyser vikten av långa
uppföljningsperioder, samt justering för effekter av omvänd kausalitet, när
effekter av ett socialt och mentalt aktiv liv undersöks i relation till kognitiv
funktion.
9
Introduction
Today, as people live longer, dementia is becoming more prevalent around
the world. According to the World Alzheimer Report (2010), 35.6 million
people currently suffer from dementia. This number is estimated to increase
to 65.7 million by 2030 and reach 115.4 million by 2050. Hence, advances in
knowledge of how to maintain levels of cognitive functioning in old age and
how to identify factors that postpone the onset of dementia diseases are of
acute interest, not only from an individual viewpoint, but also from a societal
perspective.
In the last decade, a variety of factors have been suggested as playing a
role in cognitive functioning and risk of incident dementia. Although
neurostructural factors established early in life (Raz & Rodrigue, 2006) and
genetic factors (Gatz et al., 2006) have been shown to be critical,
environmental influences are important to take into account. It has, for
example, been suggested that engagement in various leisure activities can be
beneficial for cognitive functioning. Reviews suggest that participation in
mental, intellectual, and social activities in adulthood and old age can be
helpful in reducing the risk of cognitive decline and to decrease the risk of
dementia (see Fratiglioni, Paillard-Borg, & Winblad, 2004; Stern & Munn,
2010; Wang, Xu, & Pei, 2012). Another related factor assumed to affect
cognitive functioning is social relationships. Some prior studies have, for
instance, found that having a larger number of social relationships and/or
having more frequent contact with family and friends might be beneficial for
cognitive functioning and might reduce the risk of dementia (e.g., Bennett,
Schneider, Tang, Arnold, & Wilson, 2006; Crooks, Lubben, Petitti, Little, &
Chiu, 2008; Fratiglioni et al., 2004; Zunzunegui, Alvarado, Del Ser, & Otero,
2003).
Associations found with leisure activity and social relationships are
often assumed to be in accordance with the cognitive reserve hypothesis,
suggesting that intellectual stimulation can reduce decline in cognitive
functioning, decrease the risk of dementia, and provide resources to function
normally even in the presence of dementia pathology (Scarmeas & Stern,
2003). Engagement in leisure activity and social relationships can affect
cognitive functioning through other paths than cognitive stimulation,
though. An engaged lifestyle can, for example, lower the risk of stress (e.g.
Iwasaki, Mactavish & Mackay, 2005; Ozbay et al., 2007) and depression
(e.g., Glass, de Leon, Bassuk, & Berkman, 2006; Jang, Borenstein, Chiriboga,
& Mortimer, 2005), factors that have been associated with both cognitive
functioning (for stress, e.g., Bremner et al., 1993; Golier et al., 2002, for
depression, e.g., Sachs-Ericsson, Joiner, Plant, and Blazer, 2005; Zelinski &
Gilewski, 2004) and risk of dementia (for stress, e.g., Johansson et al, 2010;
for depression, e.g., Jorm, 2001; Ownby, Crocco, Acevedo, John &
10
Loewenstein, 2006). It could also be the case that lowered activity level is a
consequence of, rather than a risk factor for, cognitive impairment (Pillai &
Verghese, 2009). More knowledge about the extent to which these factors
might generate broader effects and postpone cognitive decline and/or
dementia diseases is required. Most researchers agree that longitudinal
studies are the best way to examine what factors influence intraindividual
cognitive changes in old age. Longitudinal studies also make it possible to
measure exposure variables prior to the development of dementia diseases
(Hertzog, Kramer, Wilson, & Lindenberger, 2008).
The overarching aim of this thesis was to investigate the relationships
between leisure activity and social relationships and longitudinal changes in
memory functioning and/or risk of dementia. All three studies were
population based and included longitudinal follow-up. Studies I and II
focused on an elderly population (65-95 years), and Study III targeted
middle-aged adults (40-60 years). The first two studies were based on data
from the Betula prospective cohort study (Nilsson et al., 1997, 2004),
whereas the third study included data from both the Betula study and the
Västerbotten Intervention Programme (Norberg, Wall, Boman, & Weinehall,
2010).
The introduction to this thesis provides a background of the empirical
studies. Following the introduction, interindividual differences in cognitive
ability and risk of dementia, and unexplained causes for variance, will be
discussed. Next, I will introduce aging theories designed to explain causes of
age-related differences in cognitive functioning and/or risk of dementia. The
cognitive reserve hypothesis, in focus in all three studies, will then be
introduced. After that, the enrichment factors investigated in the thesis—
leisure activities and social relationships—will be discussed in light of the
cognitive reserve hypothesis. However, as will be argued, these factors can
also be linked to cognitive functioning through pathways other than the
cognitive reserve. Also, the possibility of a reverse relationship between
activity level and cognitive functioning, as well as other aspects that affect
the ability to live an active life, will be elaborated. An overview of memory
and dementia, and the specific constructs related to these concepts, will then
be introduced and discussed from a life-course perspective. The differences
between “normal” cognitive aging and dementia will also be highlighted.
Finally, other factors assumed to influence cognitive functioning and that
were considered in the empirical studies will be presented.
The basis of individual differences in cognitive functioning in old
age
There is a great need to explore what factors are associated with cognitive
functioning and the risk of dementia. Although declines in cognitive
functioning are observed with aging, it should be noted that there are large
11
variations between individuals, and much of these interindividual
differences in level and rate of change of cognitive functioning remain
unexplained. Important questions are (1) what factors cause these individual
differences, (2) to what extent these factors are associated with the
magnitude of age-related decline, and (3) to what extent these factors
influence the risk of dementia.
In cross-sectional data, considerable interindividual variability in
cognitive performance is observed among older adults. By presenting overall
individual performance based on all memory tests within the Betula study
(Nilsson et al., 1997; 2004), Habib, Nyberg, & Nilsson (2007) demonstrated
that the variance in test performance is large in all age groups (see Figure 1).
There are individuals in the upper end of the age spectrum that perform at a
level of many younger adults, and there is a relatively large proportion
among those 70 years and older who perform above the average level of 50–
65 year olds. Based on longitudinal analyses, the authors also found that a
minority of the older adults (70-85 years) not only performed at a high level,
but they maintained their level of performance over time. The authors found
that years of formal education was one factor that predicted this stability.
Figure 1. Principle coordinates of cognitive performance for every participant in the Betula
study as a function of age based on data collected during the second occasion between 1993 and
1995. The vertical line shows the mean of middle-aged subjects (50–65 years). Reprinted with
the permission of Taylor and Francis Group.
12
Although high initial levels of cognitive performance are
advantageous, these do not automatically guarantee protection against
cognitive deficits from a long-term perspective. Wilson et al. (2002b)
followed participants over a six-year period to investigate changes in several
cognitive abilities. The authors found that changes, in general, occur in most
cognitive domains and that change is more rapid in older people. However,
the results also showed that baseline ability was not a good predictor of the
rate of longitudinal change. The authors emphasized that although there are
large individual differences in cognitive ability, person-specific factors might
primarily contribute to changes. Moreover, in addition to major differences
in terms of baseline ability and rate of change, there are also individual
differences in cognitive ability in the presence of dementia pathology (Stern,
2002). A number of studies have shown that people who are considered as
non-demented, based on measures used in longitudinal studies, often fulfill
the pathological criteria of dementia at autopsy (Crystal et al., 1988; Morris
et al., 1996; Price & Morris, 1999).
Models of cognitive aging
There are several models proposed to account for age-related deficits in
cognitive test performances, and some of these will be considered here.
One model that attempts to account for differences in cognitive ability
is the processing speed theory. This model assumes that a decline in a
general factor—processing speed—can account for most of the age-related
decline in a variety of cognitive tasks (Salthouse, 1996), for example, in
episodic memory (e.g., Hultsch, Hertzog, & Dixon, 1990; Salthouse, 1993).
Changes in processing speed are believed to be a consequence of a general
slowing of the nervous system that comes with advanced age. However, it
should be noted that more recent longitudinal data provide little support for
the speed hypothesis (Sternäng, Wahlin, & Nilsson, 2008).
Another model of cognitive aging is the common cause hypothesis
(Baltes & Lindenberger, 1997). This hypothesis suggests that a decrease in a
common factor can predict age-related deterioration both in cognitive and
non-cognitive processes. The cognitive deficits can include all higher order
cognitive functions. The common cause hypothesis suggests that losses in a
variety of cognitive functions are significantly connected with losses in other
processes such as vision, auditory, and physical functioning. However, as
with the processing speed theory, longitudinal data do not lend strong
support for the common cause hypothesis (see Sternäng, Jonsson, Wahlin,
Nyberg, Nilsson, 2010).
Another concept used in research of cognitive aging is "successful
aging", although it does not exclusively refer to the cognitive aging process.
The concept itself is rather an umbrella term, and according to Rowe & Kahn
(1999) “successful aging” contains three main components. The first includes
13
low probability of diseases and disabilities. The second component refers
primarily to the ability to have "engagement with life," while the third
component concerns mental and physical function. Because "successful
aging" is defined in many different ways in the literature, and often reflects
what the researcher is interested in (Bowling, 2005), it should probably not
be regarded as a theory, but rather as an attempt to include the importance
of sufficient cognitive function as a vital aspect of aging.
Another model, often mentioned in the same body of research as
"successful aging", is "selective optimization with compensation" (Baltes,
1990; Riediger, Freund & Baltes, 2005). According to this theory the
subjective experience of aging is essential, and the elderly can retain function
despite cognitive decline by utilizing most of their preserved functional
abilities.
The notion that "mental exercise" can enhance general cognitive
ability has been put forward for at least a century (see Foster & Taylor, 1920;
Jones & Conrad, 1933; Thorndike, Tilton, & Woodyard, 1928), and a concept
that is widely used in the literature today is "use it or lose it" (Swaab, 1991).
This perspective assumes that mental stimulation can generate broader
effects on cognitive function and that we maintain cognitive function and,
possibly, also reduce the risk of dementia by keeping mentally active. The
idea that we should continue to keep mentally active to prevent cognitive
decline is inherent in variants of the cognitive reserve hypothesis, which is in
focus in this thesis. The concepts are overlapping, but the cognitive reserve
hypothesis often relates to the idea that an individual should be mentally
active throughout their lifespan in order to build up a reserve of cognitive
ability. The "use it or lose it" concept is rather an umbrella term that might
represent either the ideas of the reserve hypothesis or the use-dependency
theories, which makes the assumption that cognitive activity in later life is
sufficient to maintain good cognition in old age (Almond, 2010).
Furthermore, as will be noted in the next section, the term cognitive reserve
was based on the notion that cognitive stimulation could provide resources
to cope with dementia pathology (Scarmeas & Stern, 2003).
Similar to these theories is the "disuse" hypothesis that posits that
changes that occur later in life, such as retirement, loss of loved ones, and/or
loneliness, can in turn cause a loss of cognitive stimulation (for example, less
need for problem-solving), that in turn can cause reduction in fluid abilities
(e.g., Christensen et al., 1996; Paggi & Hayslip, 1999; Schooler, Mulatu, &
Oates, 1999). Although the concepts overlap, the cognitive reserve
terminology will be used in this thesis because we considered it well suited
for investigating long-term effects of cognitive stimulation. Further, as
previously noted, the cognitive reserve hypothesis aims to explain how
cognitive stimulation can provide resources to cope with dementia pathology
(Scarmeas & Stern, 2003), an outcome in focus in this thesis.
14
The cognitive reserve hypothesis
In recent years, as a consequence of increased scientific interest in
prevention and methods of treatment for dementia, the cognitive reserve
hypothesis has received considerable attention. The hypothesis (or model) is
based on evidence showing that intellectual enrichment can decrease the risk
of dementia and promote the build-up of resources to cope with dementia
pathology (Scarmeas & Stern, 2003). The concept is regularly defined as our
capacity to efficiently use our brain networks and cognitive components
(Stern, 2002), but the concept also refers to larger neural capacity and the
ability to recruit additional brain regions to compensate for age-related
cognitive changes (Tucker & Stern, 2011). An individual’s cognitive reserve is
not believed to be static, but is considered, at least partly, to be modifiable
over the entire life course. Thus, if we stimulate the brain enough, we might
build up cognitive networks that become valuable as areas of the brain start
to deteriorate (Hultsch, Hertzog, Small, & Dixon, 1999; Scarmeas & Stern,
2003; Katzman, 1993). Findings also suggest that substantively complex
tasks performed even in old age can create a capacity to deal with the
intellectual challenges from the environment (Schooler & Mulatu, 2001).
The cognitive reserve attempts to account for the fact that some
individuals with extensive neuropathology associated with dementia show
little cognitive decline when others with similar pathology show marked
deficits (Stern, 2002; Esiri & Chance, 2012). According to the model,
individuals have different amounts of reserve capacity, and as the brain
starts to deteriorate in later life the person with a higher reserve capacity is
less affected by the deterioration. It should be noted, however, that when
using pathology as a reference, little is said about reserve capacity before the
onset of pathology.
Although the cognitive reserve is frequently considered as an active
model, meaning that it can be influenced by intellectual enrichment, passive
models of the reserve should also be highlighted. The passive models are
often linked to the brain reserve concept (Katzman, 1988; 1993). A
traditional view of the passive models is that the reserve capacity represents
the hardware of the brain, and they are often linked to neuroanatomical
measures such as brain size and/or the number of neurons or synapses. This
perspective also emphasizes biological differences as one possible cause of
reserve capacity, although it does not exclude the influence of an engaged
lifestyle. Even though individuals with higher brain reserve allow a larger
loss of ”hardware” before cognitive impairment is manifested, an engaged
lifestyle might modify the pathology associated with cognitive impairment or
dementia (Valenzuela, 2008). Regardless of whether active or passive
models are considered, cognitive stimulation might cause physiological
changes to the brain.
15
Effects of cognitive stimulation on the brain
The first assumption of the cognitive reserve model, i.e., the more efficient
use of brain networks, can refer to enhanced synaptic activity or the
generation of more efficient circuits of synaptic connectivity as the result of
cognitive stimulation. The second assumption, i.e., better use of alternative
networks, refers to the ability to shift operations to other brain circuits
(Scarmeas & Stern, 2003). This notion of brain plasticity is often considered
the basis of cognitive reserve theory (Lövdén, 2010).
Valenzuela, Breakspear, and Sachdev (2007) reported in their review
that enrichment factors (e.g., various factors believed to be mentally
stimulating) can be beneficial not only to brain plasticity, but might also
promote some neurotrophic factors. Enrichment can increase neurogenesis
in the hippocampus, an area that is important for learning and memory
functioning. Intellectual enrichment has also been related to nerve growth,
which is important for regulation of cell body size and dendritic and spine
density, and further plays an important role in the pathology of Alzheimer’s
disease (AD). Furthermore, it is possible that cognitive stimulation has an
impact through breakdown of amyloid plaque, which is associated with
dementia progression. Moreover, in AD the density of neurofibrillary tangles
is greater, often in the prefrontal cortex and hippocampus. Individuals with
higher cognitive capacity earlier in life have been shown to have reduced
densities of tangles. Thus, one interpretation could be that the cognitive
reserve, as measured by prior cognitive capacity, is associated with the
spread of dementia pathology in old age (Esiri & Chance, 2012). In addition,
it has been found that education and work complexity are related to more
complex dendritic patterns (Valenzuela et al., 2007) and that cognitive
reserve indicators (e.g., intellectual activities) are associated with brain
metabolic activity and regional brain blood flow (Solé-Padullés et al., 2009).
However, although it has been suggested that cognitive stimulation might
affect the brain in many different ways, the architectural neural basis for the
cognitive reserve is still not well understood (Esiri & Chance, 2012).
Effects of leisure activity
Leisure activities can be defined as activities that individuals engage in for
enjoyment or well-being that are independent of work or activities of daily
living (Verghese et al., 2006). According to the “activity theory” (Havighurst,
1961), engagement and maintenance of activity levels are important to
maintain life satisfaction. In the last decades, increased interest has been
directed towards the relationship between leisure activity and cognitive
functioning. Many of these studies have focused on the effects of cognitive
stimulation in later life (•65 years of age) and found that frequent or
increased participation in mental, social, and/or productive activities is
associated with reduced risk of dementia and/or cognitive decline (e.g.,
16
Karp, Paillard-Borg, Wang, Silverstein, Winblad, & Fratiglioni, 2006;
Paillard-Borg, Fratiglioni, Winblad & Wang, 2009; Scarmeas, Levy, Tang,
Manly & Stern, 2001; Verghese et al., 2003; Wang, Karp, Winblad &
Fragtiglioni, 2002; Wilson et al., 2002a; Wilson et al.,2002b). In a similar
vein, studies of cognitive training (e.g., task-switching, complex video games,
divided attention) have confirmed that older adults can improve their
cognitive performance following participation in intensive training programs
(Hertzog et al., 2008). Furthermore, and in line with the cognitive reserve
hypothesis, it has been found that engagement in leisure activities in early
and middle adulthood might decrease the risk of cognitive decline and
dementia in old age (e.g., Carlson, Helms, Steffens, Burke, Potter, &
Plassman, 2008; Crowe, Andel, Pedersen, Johansson & Gatz, 2003;
Friedland et al., 2001; Fritsch, Smyth, Debanne, Petot & Friedland, 2005;
Glei, Landau, Goldman, Chuang, Rodríguez, & Weinstein, 2005; Lindstrom
et al., 2005). Kåreholt, Lennartsson, Gatz, and Parker (2011) found that
mental (e.g., reading, playing a musical instrument), socio-cultural (e.g.,
study circles, theatre), and political activities (e.g., political speeches, written
articles) undertaken between 46-75 years of age were positively associated
with cognitive functioning 20 years later in life. Taken together, a number of
reviews also propose that engagement in leisure activities over the life span
can be beneficial to maintaining cognitive ability and to reducing cognitive
decline and dementia risk (see, e.g., Fratiglioni et al., 2004; Stern & Munn,
2010; Wang et al., 2012).
While there are studies indicating that a higher activity level benefits
cognition, it should be noted that there are several studies finding no
relationship between leisure activity and cognitive functioning and risk of
dementia (see, e.g., Akbaraly et al., 2009; Aartsen, Smits, van Tilburg,
Knipscheer & Deeg, 2002; Saczynski et al., 2006; Wilson et al., 2002a; Wang
et al., 2006), and some researchers question the notion that keeping
mentally active prevents age-related decline (Salthouse, 2006). However,
there is a possibility that engagement in leisure activities might promote
cognitive functioning through other pathways. Although several of these
factors (e.g., stress, depression) will be discussed in more detail later in this
thesis, they will briefly be discussed here as possible mechanisms that can
modify the effects between leisure activity and cognitive functioning.
Other pathways from leisure activity to cognitive functioning
Apart from a direct link via cognitive stimulation, it could be that
engagement in leisure activities affects cognition through other pathways.
For example, an engaged lifestyle could potentially reduce the risk of heart
attack/cardiovascular problems, stroke, diabetes, and high blood pressure
(hypertension), factors that have all been negatively associated with
cognitive functioning (see Anstey & Christensen, 2000; Launer et al., 2000;
17
Qiu, Winblad, Viitanen & Fratiglioni, 2003). Furthermore, higher levels of
engagement in leisure activities have been related to lower risk of depression
(Glass et al., 2006) and stress (Iwasaki et al., 2005), factors that have also
been associated with cognitive functioning (for stress, e.g., Bremner et al.,
1993; Golier et al., 2002, for depression, e.g., Sachs-Ericsson, et al., 2005;
Zelinski & Gilewski, 2004) and dementia (for stress, e.g., Johansson et al,
2010; for depression, e.g., Jorm, 2001; Ownby et al., 2006). An engaged
lifestyle might also give access to a larger social network that in turn might
be beneficial to the individual’s overall health (e.g. mental and physical).
When interacting with others, we tend to adapt to social norms (HoltLunstad, Smith, & Layton, 2010) and might, therefore, change our habits
regarding, for example, smoking, alcohol use, and physical activity, all of
which are factors that can influence cognitive functioning. Thus, because
leisure activities might affect different health outcomes, they might also
affect cognitive functioning indirectly.
Reverse causality and other factors related to patterns of leisure
activity
Illnesses and poor mental and physical health that might come with higher
age can reduce the possibilities of an active lifestyle (Bukov, Maas, Lampert,
2002), and it has been found that with advanced age more time is generally
devoted to daily self-care activities that in turn reduces the time for leisure
activities (Horgas, Wilms, & Baltes, 1998; Lawton, Moss, & Fulcomer, 1987).
Furthermore, with higher age it often takes longer to solve intellectual
challenges (Clay et al., 2009; Salthouse, 1996). Hence, changes in cognitive
ability could affect patterns of leisure activity directly (Hultsch et al., 1999).
It is possible that the elderly withdraw themselves from certain leisure
activities because more time (and cognitive resources) is needed to
undertake them, and activities that used to be associated with well-being and
satisfaction instead cause frustration and become a reminder of lost abilities.
Furthermore, and most importantly, activity level can possibly also signal
preclinical symptoms of dementia because the prodromal phase is known to
be long (Amieva et al., 2008). Thus, at least in some cases, reduced
engagement might be a consequence, rather than a risk factor, for cognitive
impairment. Hence, the possibility of so-called reverse causality should be
considered when investigating the relationship between leisure activity and
cognitive functioning/dementia. Nevertheless, many studies within this field
have used a relatively short follow-up period, often less than five years,
which minimizes the possibility to adjust for reverse causality (e.g., Akbaraly
et al., 2009; Fabrigoule et al., 1995; Wilson et al., 2002a). Only a limited
number of studies have adjusted for reverse causality by having a time
period between measurement of social relations and the first cases of
dementia (e.g. Paillard-Borg et al., 2009; Wang et al., 2002)
18
Activity patterns, however, might not only be a consequence of
cognitive status. After retirement, the elderly in general have more spare
time that can be allocated to leisure activities (Gauthier & Smeeding, 2001).
Thus, elderly individuals with sufficient physical and cognitive capacity can
be motivated to increase their engagement in leisure activities.
Another factor that might relate to activity patterns is education.
Individuals with higher education are more likely to be active in social and
productive activities than the less educated (Bukov et al., 2002).
Furthermore, higher levels of education in general result in higher income
and higher socio-economic status, which are factors that might influence the
choice of activities. Furthermore, although engagement in certain leisure
activities can benefit access to social networks, it might also be the other way
around such that people with larger networks tend to be more active (Vance,
Ross, Ball, Wadley, & Rizzo, 2007). Gender differences have been observed
in regard to leisure activities, and women more often tend to engage in more
sedentary and indoor (often home-based) activities, whereas men often
prefer physical activities (O’Brien Cousins, 1998).
There might be cohort effects regarding the choice of leisure activities,
and lifestyle changes provide a variety of new activities that might attract
new generations. Furthermore, differences in earlier stages of life (e.g.,
socio-economical differences) may have provided more diverse opportunities
and interests in leisure for different individuals. It has been suggested that
new generations are more active compared to former generations due to
differences in resources earlier in life (Huber & Skidmore, 2003). Even if
there is a large variety in the number of activities nowadays, selections made
by the elderly are often restricted to factors such as personal economics,
culture, and climate (Mollenkopf, Marcellini, Ruoppila, Széman, & Tacken,
2005).
Effects of social relationships
Social relationships is a broad term that can be used to refer to structural
aspects of an individual’s social life (e.g., marital status, living status,
network size, or contact frequency) as well as functional aspects (e.g.,
received or perceived emotional support) of social relations (Holt-Lunstad et
al., 2010). Although various other overlapping concepts such as social
engagement, social integration, social support, and social network are used
in the literature (Berkman, Glass, Brissette, & Seeman, 2000), social
relationships can be regarded as an umbrella term. Furthermore, the social
network is considered as the matrix of social relationships that individuals
are tied to (Peek & Lin, 1999).
It has been suggested that characteristics of an individual’s social
relations can be regarded as indicators of intellectual enrichment and that
having a rich social life can be cognitively challenging because conversation
19
with others taps several cognitive components, including memory and
attention (Ybarra et al., 2008). Thus, preserving social connections and
being socially active has not only been regarded as an indicator of an active
lifestyle (Bassuk, Glass, & Berkman, 1999), but it has also been found to be
related to factors such as illness frequency, immunological functioning,
happiness, depression, well-being, heart disease, morbidity, and mortality
(Rook et al., 2007; Barger, 2013; Krumholz et al., 1998; Mookadam &
Arthur, 2004; Seeman & Crimmins, 2001; Tay, Tan, Diener, & Gonzalez,
2013; Uchino, Cacioppo, & Kiecolt-Glaser, 1996), with both the level and
change in cognitive functioning (e.g., Barnes, Mendes de Leon, Wilson,
Bienias, & Evans 2004; Bassuk et al., 1999; Beland, Zunzunegui, Alvardo,
Otero, & Del Ser, 2005; Ertel, Glymour, & Berkman, 2008; Fratiglioni et al.,
2004; Holtzman et al., 2004; Lövdén, Ghisletta, & Lindenberger, 2005;
Zunzunegui et al., 2003), and with reduced risk of all-cause dementia and
AD (e.g., Bickel & Cooper, 1994; Crooks et al., 2008; Fratiglioni, Wang,
Ericsson, Maytan, & Winblad, 2000; Helmer et al., 1999). However,
exceptions to these patterns exist (see Beard, Kokmen, Offord, & Kurland,
1992; Glei et al., 2005; Holwerda et al., 2012; Seeman et al., 2001).
Bennett et al. (2006) found that social network size modified the
association between AD pathology and cognitive function. Their study
showed that cognitive function was higher for participants with larger
network size even in the presence of AD pathology—a result that is in
accordance with the cognitive reserve hypothesis (Scarmeas & Stern, 2003,
Stern, 2002)—and this suggests that a larger social network provides better
resources to cope with dementia pathology.
Despite the interest in the influence of social factors in old age, limited
attention has been paid to the influence of social relations and cognitive
functioning in midlife (e.g., Green, Rebok, & Lyketos, 2008; Seeman et al.,
2011; Ybarra et al., 2008). Middle age should be of special interest because it
is characterized as a period of decreasing network size (Wrzus, Hänel,
Wagner, & Neyer, 2013), a change that could possibly exert a negative
influence on cognitive functioning. The potentially protective effects of
midlife social relations have also not been well studied regarding cognitive
impairment and risk of dementia in old age (for exceptions see, Håkansson
et al., 2009; Saczynski et al., 2006). As for leisure activities, social relations
might promote cognitive functioning through other pathways than cognitive
stimulation. Although several of these factors will be discussed in more detail
later in this thesis, they will now be briefly discussed here.
Other pathways from social relationships to cognitive
functioning
Even if positive associations between social relationships and cognitive
functioning can be explained by the cognitive reserve model (Scarmeas &
20
Stern, 2003; Stern, 2002), it is possible that social relations can affect
cognition through other pathways. Social relations can provide support that
can be informational, emotional, and/or more tangible and thus provide an
individual with useful resources to cope with stressors in life (Holt-Lunstad
et al., 2010; Ozbay et al., 2007). Furthermore, it is well known that
regulation of moods, emotions, and feelings of control are psychological
processes that can be linked to social relationships (Uchino, 2006). Thus,
social relations might be important for cognition indirectly because—as will
be presented in later chapters—cognitive abilities (Sands, 1981–1982;
Vondras, Powless, Olson, Wheeler, & Snudden, 2005) and risk of dementia
(Johansson et al., 2010) can be affected by psychological stress. Similarly,
individuals with larger social resources have reduced risk of depressive
symptoms (Jang et al., 2005), a factor that has also been associated with
cognitive decline (e.g., Sachs-Ericsson et al., 2005; Zelinski & Gilewski,
2004) and the risk of dementia (Jorm, 2001; Ownby et al., 2006).
Furthermore, social relations can encourage a healthier lifestyle (Lewis &
Rook, 1999) that in turn might influence cognitive ability (e.g., healthier food
habits, consistent exercise, not smoking) because when interacting with
others we tend to adapt social norms or engage in activities that promote a
healthier lifestyle (Holt-Lunstad et al., 2010).
Reverse causality and other factors related to patterns of social
relations
It is important to point out that reduced engagement in social relations in
old age, just as for leisure activities, might be a consequence of cognitive
impairment (see Pillai & Verghese, 2009). Furthermore, individuals, at least
in the prodromal phase of dementia, are probably less able to participate in,
or are less interested in engaging in, social relationships and thus show a
pattern of social withdrawal. Although there is an obvious risk of reverse
causality influencing the results when investigating the association between
social relations and dementia, several studies have used a relatively short
follow-up period—often not more than five years—after late-life
measurement of social relations (e.g., Beard et al., 1992; Crooks et al., 2008;
Helmer et al., 1999; Holwerda et al., 2012). Even if an adjustment for reverse
causality are motivated in studies investigating the association between
social relationships and cognitive functioning, only a limited number of
studies have a sufficiently long time frame between baseline measurement
and the occurrence of dementia (e.g., Amieva et al., 2010; Håkansson et al.,
2009; Saczynski et al., 2006).
Other factors, however, could also influence patterns of social
relationships. Deterioration in physical health might mean that more time is
needed for daily self-care activities, a limitation that might reduce the time
for social interactions (Horgas et al., 1998; Lawton et al., 1987). The loss of
21
close relationships becomes more common at older ages (e.g., death of a
spouse or friends) that can also reduce the number of social contacts. Even
though it is common to have a smaller social network size in old age, older
adults appear to play an active role in managing their social relationships,
which in turn might also affect their network size. Compared to younger
individuals, the elderly often choose to interact with individuals who can
bring them emotional reward and support because this, generally, optimizes
well-being (Rook et al., 2007). In the same vein, it has been found that the
elderly are usually more focused on attaining a desired emotional state than
their younger counterparts (Gurung et al., 2003), and it is not uncommon
that long-term bonds (e.g., with a spouse or near friends) that give more
support become more important. It has been found that in aging the range of
interaction partners often becomes narrower and more confined to an “inner
circle” often consisting of familiar partners, thus reducing the total network
size. The number of close ties, however, can still be comparable to that of
younger persons. Thus it is not certain that decreases in network size mirror
threats to well-being or health; rather, it can represent how the elderly try to
preserve health and well-being (Rook et al., 2007).
Memory systems
The memory systems will be introduced in the following section, but first a
brief background on the concept of memory will be given. The word
“memory” is frequently used in everyday life, and people often refer to their
own memory as a thing or device in terms of how well it works, and it is often
considered as place holding information of past events (Wagoner, 2012).
Morris (2007) describes perspectives of how memory is thought of in the
neurosciences, which to some extent summarizes many common views on
memory as a scientific object today. In the neurosciences, memory has been
defined as (1) the ability to encode, store, consolidate and retrieve
information, (2) as a hypothetical store where information is kept, (3) as the
information that is stored, but not in physical terms such as engrams or
memory traces, (4) as the process that allows us to retrieve information from
where it is stored, including the ability to do something that we have just
learned, or (5) as our ability to consciously know that we remember things.
Traditionally, two common orientations have been used to describe memory.
One perspective proposes multiple memory systems, and the other focuses
on mental processes in terms of dynamic patterns in neural activity or by
synaptic functionality with regard to how we encode or retrieve memories.
There is considerable overlap between the current understandings of these
two perspectives.
22
Declarative and nondeclarative memory
Ryle (1949) identified two forms of long-term memory systems based on
what he considered as “knowing what” and “knowing how”. Memories that
consist of factual knowledge and personal events (i.e., memories that we
encode, store, and later consciously recall) were labeled by Ryle as
declarative memories. Memories that are unconscious to us (e.g., learning
how to ride a bicycle) but that are necessary to deal with everyday living he
labeled as nondeclarative memories. Today, declarative and nondeclarative
memory is sometimes referred to as explicit and implicit memory. Of
primary interest in this thesis is declarative rather than nondeclarative
memory. Declarative memory has been further divided into two sub-forms or
systems.
Episodic and semantic memory
Nielsen (1958) stated that there were two separate "tracks" in the brain for
two different types of conscious memory, one for time and events that
revolves around the person, and one for knowledge, often acquired through
study. A similar two-part division came to be known as episodic and
semantic memory (Tulving, 1972, 1983), concepts still in use today. The
episodic memory, which is a rather fluid ability (i.e., less dependent of
acquired knowledge), is assumed to hold memories that refer to a particular
event or place and to our ability to mentally be able to travel backward and
forward in time. Thus, episodic memory refers to memory of personally
experienced events such as what we had for breakfast or what we did on
vacation. Episodic memory is sometimes divided into subcomponents such
as recall and recognition (Nyberg et al., 2003). Recall refers to our ability to
recollect information from the past in the sense that we have a mental
representation that makes it possible for us to talk about earlier events and
processes. Recognition, on the other hand, refers to our ability to make a
judgement that a repeated event corresponds to a past event (Saumier &
Chertkow, 2002).
Semantic memory—which is a crystallized ability (i.e. dependent on
acquired knowledge)—deals with facts, ideas, meanings, and concepts as well
as an understanding of how parts of knowledge are related. Semantic
memory is dependent on what knowledge a person has gained throughout
life, for example, through education. It can, for instance, concern
geographical and historical knowledge (Passer & Smith, 2007) but also
includes linguistic skills and the understanding of letters and symbols.
Semantic memory, just as episodic memory, is sometimes divided into
subsystems. Nyberg et al. (2003), for example, proposed a distinction
between semantic knowledge and semantic (word) fluency.
In summary, one can say that episodic memory enables us to move
ourselves mentally to a particular event or place, both in the past and in the
23
future (autonoetic), whereas semantic memory is only conscious knowledge
that might not be related to any past or future event or location (noetic)
(Tulving, 1972, 1983).
Episodic and semantic memory functions in normal aging
Based on mean performance in several cognitive tasks, longitudinal data
from the Betula study (Rönnlund, Nyberg, Bäckman, & Nilsson, 2005) have
indicated that episodic memory ability is relatively stable until the age of 60
to 65, after which a gradual age-related decline is observed. This is in
contrast with cross-sectional data (e.g., Park, Lautenschlager, Hedden,
Davison, & Smith, 2002) that have suggested that several cognitive abilities
(e.g. free recall, cued recall) show an approximately linear decrease from
early to late adulthood with an onset of decline as early as age 20 or 30. The
magnitude of age-related memory differences and changes might, however,
depend on whether recall or recognition is considered as the outcome
measure (Spencer & Raz, 1995). More specifically, it has been argued that
aging generally has a less pronounced negative effect on measures of
recognition than on recall (e.g., Botwinick & Storandt, 1980; Craik, 1977)
because recognition tasks require less processing resources (Craik &
McDowd, 1997). An alternative explanation is that older adults generally use
less strategic search methods for recall compared to younger individuals. It
has also been suggested that the elderly, in general, recollect more
integrative and interpretive information from an event, whereas younger
people have a better ability to recollect more specific details of the to-berecalled event (Zacks & Hasher, 2006). Apart from putative differences
between subsystems of episodic memory, age-related decline in episodic
memory appears to be a part of normal aging. Finding methods to enhance
performance, however, or to at least postpone a decline in episodic memory
abilities, is important because impairment in episodic memory functioning is
also a common feature of dementia pathology (Cabeza, 2004). Thus, factors
that strengthen episodic memory functioning should be identified.
Turning to semantic memory, Rönnlund et al., (2005) showed
longitudinal increments in average performance levels from the age of 35 up
to young-old age (age 65), after which a modest decline was observed such
that the old-old adults (80 years or older) were still almost at a level of 35year-olds. It has also been suggested that tasks that require fast retrieval of
semantic information, such as rapid generation of words beginning with one
particular letter (letter fluency), are more age sensitive than other semantic
knowledge tasks (see Nyberg et al, 2003; Zacks & Hasher, 2006). However,
it should be noted that such differences might be due to a slowing in the
speed of information retrieval rather than to a loss of semantic
representations. Deterioration in speed is relatively common at older age
(Clay et al., 2009; Salthouse, 1996), and, thus, it possible that semantic
24
knowledge remains relatively stable over the life span, once speed is adjusted
for.
Working memory
Working memory can be defined as a limited system that deals with
temporary storage and manipulation of information (Baddeley, 2000).
Working memory is highly useful in a variety of everyday situations such as
reading, driving a car, remembering telephone numbers, etc. Working
memory is assumed to underlie several complex mental processes and has
historically been regarded as a system critical to our ability to plan and act in
different situations (Miller, Galanter, & Pribram, 1960). Working memory is
generally assumed to include both storage and processing components, and
the latter can be seen as something that distinguishes working memory from
short-term memory, which has been considered as a component that
temporarily holds a limited amount of information (see Atkinson & Shiffrin,
1968). Although alternative interpretations of how we handle temporary and
conscious information have been put forward throughout history, and
concepts such as primary memory, focus of attention, or selective attention
have been used (see Broadbent, 1958; Cowan, 2005; James, 1890; Unsworth
& Engle, 2007), working memory is a commonly used term.
A classical model of working memory was introduced by Baddeley and
Hitch in 1974 in which they considered working memory as a
multicomponent system. A more refined model (Baddeley, 2000) includes
one control system comprising the executive function and three slave
systems comprising the phonological loop, the visuospatial sketchpad, and
the episodic buffer. The executive function manages and manipulates our
attention with the help of the slave systems and is considered to be a higher
cognitive function because the slave systems are expected to operate under
the executive function. Because the central executive function serves to
regulate the flow of information within the working memory system, it is an
important component in the control of our thoughts and actions. The slave
systems can only maintain and manipulate a limited amount of information
at a time. The phonological loop stores phonological information, and by
using subvocal rehearsal it can help us hold information for a short time. The
visuospatial sketchpad maintains and manipulates visual and spatial
information, and the episodic buffer, also limited in its nature, serves as a
multi-dimensional coding system. The episodic buffer is believed to integrate
information from both the visuospatial sketchpad and the phonological loop,
as well as information from the sense organs and the long-term memory, and
to bind together information from the various components as
multidimensional representations (chunks) that enable us to understand and
make use of the information.
25
With regard to this thesis, a further description of working memory
will be restricted to processing of visuospatial information because this is the
only component of the memory systems that was measured in the three
studies.
Visuospatial ability
Visuospatial ability refers to our ability to manipulate and handle visual and
spatial information (Baddeley, 2000) and to be able to visually perceive and
understand spatial relationships between objects (Lohman, 1988). It has,
furthermore, been proposed that the visuospatial sketchpad can be divided
into a visual cache and an inner scribe. The visual cache, a relatively passive
component, refers to our ability to hold information about, for example,
color and shape. The inner scribe is a rather active component that helps us
deal with spatial and movement information (Mammarella, Pazzaglia &
Cornoldi, 2008). However, the distinction between these components is not
straightforward, and visuospatial tasks require both components to be active
(Klauer & Zhao, 2004).
It should also be pointed out that in models of cognitive abilities based
on factor-analytic approaches (for example, the hierarchical model by Carroll
(1993), visuospatial ability is considered as a separate ability factor (general
visualization) that, together with other second-order cognitive factors, is
subsumed by a general ability factor.
Visuospatial ability in normal aging
In old age there seems to be a pronounced age-related deterioration in the
efficiency and speed of handling visuospatial material compared to, for
example, verbal information. In addition, when cross-sectional data are
considered, visuospatial ability seems to show a relatively early age of onset
of decline (e.g., Kaufman, 1989; Park et al., 2002). In contrast, longitudinal
data regarding spatial orientation suggest that visuospatial performance can
be relatively stable until reaching 40 years (see Sands, Terry, & Meredith,
1989) or even up to age 55 (see Rönnlund & Nilsson, 2006a). The fact that an
onset of decline in visuospatial ability is observed at higher ages might be
due to the fact that spatial tasks are heavily dependent on right parietal lobe
functioning (see Warrington, James & Maciejewski, 1986). With increased
age, there seems to be a redistribution of activity to prefrontal regions when
solving intellectual problems (Davis et al., 2008), and it has been suggested
that this change in activation can be a part of compensatory processes that
comes with aging.
Differentiating dementia progress from normal aging
Considering the consequences of dementia diseases, differentiating
“abnormal” from “normal” cognitive aging as early as possible is crucial to
26
allow deployment of all available resources to postpone the onset of
dementia. Some differences have been identified. A meta-analysis by
Bäckman et al. (2005) revealed impairment across several cognitive
functions, and these impairments were most pronounced in episodic
memory, executive functioning, and perceptual speed prior to an AD
diagnosis. Deficits were also observed in measures of attention and verbal
and visuospatial abilities, although these impairments were less pronounced.
The results of their analysis also indicated that individuals who developed
AD experienced cognitive deficits several years before the clinical diagnosis.
Results on the Mini-Mental State Examination (MMSE), an indicator of
global cognitive functioning and a composite of different cognitive abilities,
follow the same pattern. The subscales of the MMSE—orientation in time,
orientation to place, and delayed recall—are the most significant in
predicting AD three years before diagnoses. Thus, the ability to identify the
risk of AD increases significantly when tasks are assessing multiple cognitive
domains. Studies have also reported preclinical signs prior to other dementia
diseases, such as impairment in memory (including subscales of MMSE) and
fluency abilities prior to onset of vascular dementia (VaD) (e.g., Ingles,
Wentzel, Fisk, & Rockwood, 2002; Jones, Jonsson Laukka, Small,
Fratiglioni, & Bäckman, 2004) and in tasks related to frontal systems prior
to frontotemporal dementia (Geschwind et al., 2001). Although quickness in
thinking and remembering deteriorates in normal aging, it should be noted
that it is still, compared to abnormal aging, possible to learn new
information. In addition, even if it might become more difficult to switch
between and handle multiple tasks, such increased task demands are often
manageable. Further, it is still possible to use strategies, and make
behavioral changes, to compensate for cognitive changes.
The complexity of differentiating dementia progress from normal
aging must, however, be highlighted. As previously described, a decline in
some abilities (e.g., episodic memory and visuospatial ability) is usually a
part of normal aging. In addition, age-related changes in thinking and
remembering and dementia pathology can often occur simultaneously and
can be very subtle (Insel & Badger, 2002). Thus it is sometimes difficult to
determine if changes, at least in early stages, are pathological or not. In
addition, neural changes often exhibit a slow progress prior to dementia
(Bäckman et al, 2005), and it is possible that a certain threshold must be
reached before cognitive impairment is manifest in test performance.
Furthermore, impairment in memory functioning in old age is not
necessarily indicative of dementia; it can also be a sign of depression or
delirium, and this makes diagnosis difficult (Insel & Badger, 2002).
In conclusion, although there are features differentiating normal aging
from dementia progress, increased knowledge of what more precisely
27
characterizes those who have, or have not, entered the transitional state is
needed.
Dementia disorders
The most common cause of accelerated cognitive decline, at least among the
older part of the population, is dementia, and the incidence of dementia
increases after the age of 65 (Wimo et al., 1997). Dementia is a general term
that refers to many different diseases that cause long-term reductions in the
ability to think and reason normally, changes that, in turn, lower everyday
functioning and gradually make independent living impossible. Dementia is
characterized by a progressive decline in several cognitive abilities, including
memory, learning ability, thinking, language skills, judgment, and
perception. Dementia can additionally result in severe behavioral and
emotional disorders (Duguè et al., 2003; Salomon & Budson, 2011).
Dementia diseases are chronic and progressive disorders that affect large
parts or regions of the brain, and these changes cause psychopathological
symptoms. The cognitive deficits and behavioral changes depend on the
localization of the disease in the brain (Kurz & Lautenschlager, 2010). Thus,
dementia is not considered to be a disease in itself, but rather as a syndrome
and a term reflecting multiple cognitive deficits (see DSM IV; American
Psychiatric Association, 2000). In the latest version of DSM (see DSM V;
American Psychiatric Association, 2013) the term dementia has been
replaced by major neurocognitive disorder and mild neurocognitive disorder.
Despite this recent change in terminology, however, the term dementia is
still widely used, for instance, by the Alzheimer's Association. The term
dementia was, therefore, used in this thesis.
All-cause dementia
Considering dementia, regardless of form (all-cause dementia) is motivated
in studies where one wants to examine how different factors are related to
dementia. Although there is support for distinguishing among sub-types of
dementia, it has recently been shown that vascular symptoms are relatively
common even among those who develop neurodegenerative diseases
(Grinberg & Heinsen, 2010; Korczyn & Vakhapova, 2007). The risk for
vascular changes increases as age increases, and thus an interaction with
neurodegenerative diseases is more common. Therefore, mixed causes of
dementia might be more common than previously thought (Langa, Foster, &
Larson, 2004). In fact, there are those who argue that mixed causes
extremely common and that most cases of dementia are based on mixed
causes (Korczyn, 2002a; Strozyk et al., 2010; Schneider, Arvanitakis, Bang &
Bennett, 2007). According to Grinberg & Heinsen (2010) mixed brain
pathologies might be behind as many as 73% of all cases of AD and VaD.
Based on these findings, it may be difficult to judge which of the two diseases
28
are most influential in cases of cognitive impairment. The overlap between
dementia forms could possibly be seen in the light of a continuum, with AD
(or similar) pathology at one end and VaD at the other (Kalaria, 2002), and
the main cause of incident dementia could then be determined depending on
the position on this continuum.
It should be noted that there is an overlap in terms of risk factors for
different types of dementia. Apolipoprotein (APOE) İ4, for example, is not
only a risk factor for AD (Farrer et al., 1997; Smith, 2002), but it has also
been associated with vascular diseases (e.g., Frisoni et al., 1994; Stengard et
al., 1995; Treves et al., 1996). Other factors such as age, poor education,
coronary artery disease, hypertension, and smoking have been associated
with increased risk of both AD and VaD (Korczyn, Vakhapova, & Grinberg,
2012). Thus, given that there might be an overlap between types of dementia,
and that some risk factors are shared (some of which possibly can be
explained by the overlap), it might be important to investigate dementia as a
general outcome (all-cause dementia) in analyses where one wants to
examine how different factors are related to dementia. For this study,
irreversible and progressive dementias included in analyses of all-cause
dementia are forms AD, VaD, Lewy body dementia, frontal lobe dementia,
Parkinson’s dementia, and unspecified dementia.
Alzheimer’s disease
AD is considered to be a late-life disease (Dugué et al., 2003) and is often
considered to be the most common cause of dementia (Bird, 2008; Corder et
al., 1993) accounting for about 60–70% of all dementia cases (Fratiglioni et
al., 2007). It is a neurodegenerative disease characterized by a progressive
loss of intellectual and cognitive abilities. For example, episodic memory is
often impaired in AD (Cabeza, 2004). There are some known physiological
factors regarding how AD affects the brain, including loss of brain neurons
and synapses (Wenk, 2003), brain atrophy, reductions in brain activity,
reduced blood flow in certain brain areas, reduced glucose metabolism,
higher levels of amyloid-beta plaques and neurofibrillary tangles, and
increased white-matter hyperintensities (Bäckman, Jones, Berger, Laukka, &
Small, 2005). In addition to physiological aspects, AD is often accompanied
by depressive symptoms, anxiety, irritability, aggression, apathy, delusions,
and sleep and eating disturbance (Mirakhur, Craig, Hart, Mcllroy, &
Passmore, 2004). AD will, in the end, impair the ability to manage everyday
life. Although the severity and the speed of progression of AD differ
substantially between individuals, the clinical course for the disease is well
established (McDowell, 2001). To obtain a clinical diagnosis of AD according
to the DSM-IV (4th ed., American Psychiatric Association, 2000), it is
required that impairments are present in more than one cognitive function.
29
During the early stages of the disease, patients often suffer from
modest forms of memory loss and tend to have difficulties with use of
language and attention. As the disease progresses, cognitive abilities are
impaired further, and the individual will eventually become dependent on
help to handle what were previously considered to be simple everyday tasks.
Unfortunately the person might additionally face significant personality and
behavioral changes, and it is not unusual that a person with AD wanders
around aimlessly, sometimes resulting in leaving their home and being
unable to return. When AD is exhibited in its worst form, the person is no
longer able to speak normally and can no longer understand what others say,
and the AD patient requires constant care to cope with basic everyday tasks
such as eating, moving, toilet visits, and washing (Sloane et al., 2002). Today
there is no cure for AD. However, because treatment might slow its
progression, early diagnosis is important (Dugué et al., 2003).
Vascular dementia
VaD is considered the second most common form of dementia, and the risk
of VaD increases significantly with increased age (Middleton, Grinberg,
Miller, Kawas, & Yaffe, 2011). Recent studies indicate that the prevalence of
VaD might have been underestimated and that cognitive impairments
related to VaD are more common than previously thought (Grinberg &
Heinsen, 2010; Korczyn & Vakhapova, 2007; Roman, 2002). It has also been
shown that cardiovascular pathology can often interact with other
neurodegenerative diseases (Korczyn 2002a, 2002b). VaD can be caused by
a stroke or damage to multiple blood vessels in the brain. Although some
damage to blood vessels and nerve fibers in the brain is a relatively normal
part of aging, such damage can cause different forms of brain tissue lesions
(Kalaria, 2002). VaD can also be caused by hemorrhage, infarction,
hippocampal sclerosis, cortical laminar necrosis, and white-matter lesions
(Grinberg & Thal, 2010).
There are some characteristics of VaD, including impaired attention
(working memory and executive functioning), worsening memory of
contextual information, difficulties with planning and problem solving,
impaired motor and information processing, and worsening language and
writing skills. Disorientation and behavioral and emotional disturbances are
also relatively common (Sachdev et al., 2004; Nordlund et al., 2007).
Although they have difficulties with recall, VaD patients often have better
recall abilities compared to AD patients (Korczyn, Vakhapova & Grinberg,
2012). VaD also manifests as deterioration of memory capacities similar to
that of normal aging, although with greater impairment (Cabeza, 2004). The
cognitive reductions might initially be observed as mild changes and then
progressively get worse. The gradual impairment can, in some cases, be
slower than in AD (Bruandet et al., 2009). However, the cognitive changes
30
depend on the extent of the injury and the parts of the brain that are
affected. Brain lesions are often heterogeneous, causing a range of cognitive
deficits, and abilities can deteriorate cumulatively based on ongoing lesions
(Korczyn et al., 2012). Today, the term ‘vascular cognitive impairment’ is
commonly used to describe the severity of VaD progression. As for AD, VaD
can vary from mild to severe, and some consider such terminology a useful
tool to describe differences among patients (Roman et al., 2004). Although
changes in lifestyle behaviors can possibly mitigate or decrease some risk
factors associated with VaD, there is no cure for VaD.
Other dementias
Other dementias include frontal lobe dementia (FTD), Lewy body dementia
(DLB), Parkinson's dementia (PDD), and unspecified dementia (NUD). FTD
is an age-related form of dementia, although it often starts earlier than other
dementia diseases, sometimes between 45 and 65 years of age (Snowden,
Neary & Mann, 2002). The term is used to describe dementias producing
focal lobar atrophy in the frontal lobe and/or temporal lobes (Brun et al.,
1994; Miller et al., 1997) and can have a long clinical course (McKhann et al.,
2001; Seelaar, Rohrer, Pijnenburg, Fox & van Swieten, 2011). Differentiating
FTD from other dementia diseases, for example AD, is dependent on the
brain region that is affected (Hultch & MacDonald, 2004). For example,
compared to AD, in which episodic memory is impaired due to reductions in
hippocampal regions, FTD patients often show difficulties in tasks believed
to draw heavily on frontal lobe functioning such as working memory and
executive functioning (Cabeza, 2004; Seelaar et al., 2011), and deficits in
other memory capacities are not prominent features of FTD (Brun et al.,
1994; Rosen, Hartikainen & Jagust, 2002). FTD patients often suffer from
problems with both expressive and receptive language and with changes in
personality and behavior expressed as disinhibition, withdrawal, and apathy
(McKhann et al., 2001; Rosen et al., 2002), although these symptoms are
common in other dementias as well (Braaten, Parsons, Mccue, Sellers, &
Burns, 2005).
Regarding PDD and DLB, the mental state can vary considerably in
the patient from one moment to the next (Emre et al., 2007; McKeith et al.,
2005). Both PDD and DLB are neurodegenerative conditions having many
biological factors in common. Some researchers have used the umbrella term
“Lewy body dementias” to refer to both PDD and DLB because in both
conditions protein accumulations—so-called “Lewy bodies”—are often found
in the brain (Tröster, 2008). PDD is characterized by a lack of dopamine
(Cabeza, 2004), but this deficiency is also present in DLB. DLB is manifested
as a significant loss of the neurotransmitter acetylcholine (Sarter & Bruno,
1998; Shiozaki et al., 1999). DLB is often accompanied with AD
neuropathology (Marui, Iseki, Kato, Akatsu, & Kosaka, 2004), and many
31
Lewy body disorders include the presence of plaques and tangles similar to
those seen in AD (Kaufer and Tröster 2008). Both DLB and PDD often result
in difficulties with handling three-dimensional information, and visual
hallucinations are relatively common (Aarsland, Andersen, Larsen & Lolk,
2003; Weintraub & Hurtig, 2007). Although attention abilities often become
affected, memory impairment is not always persistent in the early stages of
DLB and PDD. It is not uncommon for people with DLB or PDD to fall and
suffer fractures. Blood pressure instability, sleeping problems, and
depression are also common among these patients. Although there is a
significant overlap, some features often associated with DLB are cognitive
changes that interfere with social and occupational function and unexplained
loss of consciousness, whereas PDD is often accompanied by impaired
executive functioning, problems with word-finding, impaired memory for
recall, apathy, and changes in mood and personality (McKeith et al., 2005;
Emre et al., 2007).
Additional factors associated with cognitive functioning and risk
of dementia
The main purpose of this chapter is to describe how different factors, in
addition to leisure activities and social activities, are related to cognitive
function and risk of dementia. A short empirical background is presented for
each of these factors in order to justify the choice to include these in the
three studies that make up this thesis. The background information might
not be of special interest only when interpreting the results in relation to
leisure activity and social relationships. Although many of these factors can
be related to each other, these relationships will only be briefly discussed.
Demographic factors
Aging is, undoubtedly, a major risk factor for cognitive decline and
dementia. Even though there are many psychosocial factors that can
influence cognitive aging, it is unavoidable that the biological aging process
of the brain has a large impact (Bäckman & Nyberg, 2010). Although
trajectories for different cognitive abilities seem to differ between different
memory abilities over time on a general level, a decline in several cognitive
abilities is still a common part of the aging process. In addition, the aging
process is by nature accompanied by many changes both in brain structure
and brain activity, changes that can ultimately have negative effects on
cognitive function (Raz et al., 2010). Changes in brain activity that come with
aging can also reflect brain plasticity that serves to preserve cognitive
functions (Lövdén, 2010).
A number of studies have also investigated gender differences in
relation to dementia risk. The results have been mixed, and some studies
suggest that women are at higher risk of all-cause dementia than men (e.g.,
32
Brayne et al., 1995) whereas others find no sex difference (Bachman et al.,
1993). It has also been reported that women are at higher risk of AD (Gao,
Hendrie, Hall, & Hui, 1998), but with no gender difference in risk of VaD
(Andersen et al., 1999). In addition, differences in certain cognitive abilities
have been reported. It is generally assumed that women perform better on
episodic memory tasks (e.g., Herlitz et al., 1997; Lewin & Herlitz, 2002) and
on measures of verbal ability (e.g., Hooren, Valentijn, Bosma, Ponds, van
Boxtel, & Jolles, 2007; Hyde & Linn, 1988), while men tend to perform
slightly better on tests measuring visuospatial ability such as mental rotation
tasks (e.g., Colom, García, Juan-Espinosa, & Abad, 2002; Rönnlund &
Nilsson, 2006a). In tasks that measure both visuospatial and verbal skills
(e.g., pointing out an object's previous location), however, women show a
tendency to perform better than men, probably because women use their
verbal ability to remember the location of the object (Herlitz, Lovén, Thilers,
& Rehnman, 2010). It has also been suggested that among the oldest old
(85+ years), women perform better on tasks measuring memory (word-list)
and cognitive speed (Van Exel et al., 2001).
Gender differences also appear to be stable over time. For example, de
Frias, Nilsson, and Herlitz (2006) found that over a 10-year period women
performed better on tasks measuring semantic fluency, episodic recall, and
recognition, whereas men performed better on a measure of visuospatial
ability (WAIS-R Block Design test), and these differences were stable over
time. In a similar vein, Gerstorf et al., (2006) reported a parallel longitudinal
decline in men and women in old age (70–100 years), with women showing
higher overall levels of performance across the cognitive domains (episodic
memory, verbal fluency, verbal knowledge, and perceptual speed). The
authors concluded, however, that gender differences could be masked by
differences in sample attrition.
Educational level is often regarded to be one of the major components
that contributes to the cognitive reserve (Stern, 2002), and many studies
have found that educational level is associated with reduced risk of cognitive
impairment and dementia (e.g., Brayne & Calloway, 1990; De Ronchi,
Fratiglioni, Rucci, Paternico, Graziani, & Dalmonte, 1998; Evans et al., 1997;
Gatz, Svedberg, Pedersen, Mortimer, Berg, & Johansson, 2001; Katzman,
1993; Mortel, Meyer, Herod, & Thornby, 1995; Stern, Gurland, Tatemichi,
Tang, Wilder, & Mayeux, 1994). A meta-analysis (Valenzuela & Sachdev,
2006) of cohort studies suggests that higher levels of education, together
with other beneficial lifestyle factors, reduces the risk of future dementia. In
their literature review, Sharp & Gatz (2011) reported, however, that even if
lower education has been related to higher risk of dementia, many studies do
not support this assumption, and associations found between education and
dementia may depend on the specific study populations used. Furthermore,
the relationship with dementia seemed to be stronger when years of
33
education reflected cognitive capacity. The authors suggested, therefore, that
future studies would benefit from using a lifespan perspective when
including education into their models. Studies have also found that
educational attainment attenuates cognitive decline among non-demented
elderly (e.g., Albert et al., 1995; Butler, Ashford, & Snowdon, 1996; Evans et
al., 1993; Farmer, Kittner, Rae, Bartko, & Regier, 1995; Lyketsos, Chen &
Anthony, 1999), although more recent studies have indicated a relationship
to levels of cognitive functioning but not to rate of cognitive decline
(Christensen, Hofer, MacKinnon, Korten, Jorm, & Henderson, 2001;
Glymour, Weuve, Berkman, Kawachi, & Robins, 2005; Tucker-Drob,
Johnson, & Jones, 2009; Van Dijk, Van Gerven, Van Boxtel, Van der Elst, &
Jolles, 2008; Wilson, Hebert, Scher, Barnes, Mendes de Leon, & Evans,
2009). This pattern seems to hold for several cognitive abilities such as
processing speed, working memory, verbal fluency, and verbal episodic
memory (Zahodne et al., 2011).
Finally, marital status is a factor that has been associated with a
number of health aspects. Married people, compared to those who are
divorced, single, widowers, or widowed, tend to have lower levels of
mortality (Ikeda, Iso, Toyoshima, Fujino, Mizoue, & Yoshimura, 2007;
Kiecolt-Glaser & Newton, 2001), higher self-reported health (Mercenes &
Sheiham), better cardiovascular functioning (Carels, Sherwood, &
Blumenthal, 1998), and lower levels of depression (Kiecolt-Glaser & Newton,
2001), factors that, in turn, might affect risk of cognitive decline and
dementia (e.g., Anstey & Christensen, 2000; Jorm, 2001; Ownby et al.,
2006). Living in a partnership has also been proposed to increase cognitive
stimulation (van Gelder et al., 2006), and could, therefore, contribute
directly to the cognitive reserve (Scarmeas & Stern, 2003; Stern, 2002).
Several studies have shown that not living with a partner is associated with
cognitive decline (Van Gelder, Tijhuis, Kalmijn, Giampaoli, Nissinen, &
Krombout, 2006) and dementia (Bickel & Cooper, 1994; Håkansson et al.,
2009; Helmer et al., 1999; Sundström, Westerlund, Mousavi-Nasab,
Adolfsson, & Nilsson, 2014). In addition, living in a partnership might have
positive effects on specific forms of memory. For example, Mousavi-Nasab,
Kormi-Nouri, Sundström, and Nilsson (2012) found, by following
participants over a period of five years, that married people have better
cognitive ability in terms of episodic memory. Furthermore, the rate of
cognitive decline was greater in those who were single (not living in a
partnership), an outcome that was attributable to both middle-aged (35–60
years) and older (65–85 years) individuals.
Health
As previously noted, many illnesses and poor general health that come with
higher age reduce the possibilities of engagement in activities (Bukov, Maas,
34
Lampert, 2002), a limitation, that in turn, can have a negative influence on
cognitive functioning. Still, although it has been difficult to relate several
objective (e.g., glucose level, hemoglobin value) and subjective (e.g., feeling
healthy or not) measures of health directly to cognitive functioning (see
Nilsson et al., 1997), there still are a number of health factors that have been
associated with cognitive functioning. Cardiovascular risk factors such as
heart attack/cardiovascular problems, diabetes, high blood pressure
(hypertension), and stroke have all been negatively associated with cognitive
functioning (see Anstey & Christensen, 2000; Bäckman, Jones, Small,
Aguero-Torres, & Fratiglioni, 2003; Launer et al., 2000; Qiu et al., 2003),
and it should be noted that bad health, in general terms, seems to affect fluid
abilities more than crystallized abilities (Anstey & Christensen, 2000). In
addition, having several different diseases at the same time might have even
more negative impacts on cognitive ability and risk of dementia, and because
aging is often accompanied by multiple diseases (Cauley, Dorman, &
Ganguli, 1996), the risk of cognitive deficits can increase due to this
situation. Even if it is assumed that the biological aging process itself has the
largest influence on variation in cognitive ability, while also taking into
account diseases that become more common with increasing age (Wahlin,
2004), it might still be that the age factor is overestimated. By
overestimating the effect of age in general, it is plausible that various health
aspects that might affect cognition are overlooked (Spiro & Brady, 1998).
Another health-related factor is being overweight or obese, and it is
well known that being overweight can have several adverse health outcomes.
Although few in number, there are studies that suggest that increased body
weight (body mass index (BMI) > 30) is associated with worsening cognitive
function (Elias, Elias, Sullivan, Wolf, & D'Agostino, 2003; Elias, Elias,
Sullivan, Wolf, & D'Agostino, 2005) and has a negative influence on
executive and visuomotor abilities (Wolf, Beiser, Elias, Au, Vasan, &
Seshadri, 2007). Obesity is also believed to increase the risk of dementia. It
has, for example, been found that people who are obese in middle-age are at
higher risk of future dementia (Whitmer, Gunderson, Barrett-Connor,
Quesenberry, & Yaffe, 2005) and that this might be an indicator of future
dementia as much as three decades before onset (Whitmer, Gustafson,
Barrett-Connor, Haan, Gunderson, & Yaffe, 2008). It has also been
suggested that obesity in middle age (BMI > 30) is a risk factor not only for
dementia in general, but also for AD (Kivipelto et al., 2005). A systematic
review and meta-analysis conducted by Beydoun, Beydoun, and Wang
(2008) provided overall support for an association of obesity with all-cause
dementia and AD. Still, the associations were moderate and the authors
concluded that more studies are needed.
Despite these patterns, there is a paradox regarding the relationship
between obesity and dementia. Fitzpatrick et al. (2009) found, in agreement
35
with the aforementioned studies, that midlife obesity could be related to
future dementia. However, the authors also found that in old age the
relationship was reversed. More specifically, older individuals classified as
obese were found to have a reduced risk of dementia, while underweight
people were at a higher dementia risk. This reversed relationship in old age
might reflect the fact that dementia is often characterized by weight loss.
Other studies also provide support for reduced BMI being related to
increased risk, at least for AD (Buchman, Wilson, Bienias, Shah, Evans, &
Bennett, 2005; Johnson, Wilkins, & Morris, 2006).
Stress is another health aspect that can be associated with cognitive
functioning. Chronic stress can contribute to the development of diseases by
causing increased vulnerability in individuals (Marin et al., 2011). Prolonged
stress might, for example, increase the risk of depression (Hammen, 2005)
and cardiovascular diseases (Cohen, Janicki- Deverts, & Miller, 2007), which
in turn might have adverse effects on cognitive performance. High levels of
stress, however, can also have a direct link with impaired memory function.
Cognitive abilities can be negatively affected by long-lasting stress (Bremner
et al., 1993; Gil, Calev, Greenberg, Kugelmass, & Lerer, 1990; Golier et al.,
2002), stressful life events (e.g., Sands, 1981–1982; Vondras, et al., 2005),
stress-related exhaustion (Öhman, Nordin, Bergdahl, Slunga Birgander, &
Stigsdotter Neely, 2007; Sandström, Nyström, Lundberg, Olsson, & Nyberg,
2005; Van der Linden, Keijsers, Eling,&Van Schaijk, 2005), and high levels
of cortisol, a biomarker of stress (e.g., Li et al., 2006; Lupien et al., 1998),
although consistent results are not observed across the board (e.g., Comijs et
al., 2010; Csernansky et al., 2006; Kohler et al., 2010). Studies have also
shown that the hippocampus, which is important for memory functioning, is
adversely affected by prolonged stress (Bannerman et al., 2004; Gould &
Tanapat, 1999; McEwen, 2000; Sapolsky, 2000) and that stress can be
related with reduced neurogenesis in the hippocampus (Gould & Tanapat,
1999; Sousa, Lukoyanov, Madeira, Almeida, & Paula-Barbosa, 2000).
Johansson et al. (2010) investigated whether midlife psychological
stress was related to later life dementia (AD, VaD, and others) among
women. Stress was measured at three occasions in midlife with 6-year
intervals. The results revealed that the risk of future dementia significantly
increased with the number of stressors reported. Sundström, Rönnlund,
Adolfsson, and Nilsson (2014), however, found no association between
negative or positive life events and risk of dementia.
Finally, another factor that has been negatively related to cognitive
functioning is depression. Even though it is associated with decreased
physical, social, and mental activity (e.g., Wilson, Barnes, & Bennett, 2007;
Elias & Wagster, 2007), it is a preclinical symptom of dementia, and it is a
common feature among dementia patients (Ballard et al., 2000; Park et al.,
2007), a number of studies argue that depression also increases risk of all36
cause dementia and AD (for reviews and meta-analyses see Diniz, Butters,
Albert, Dew, & Reynolds, 2013; Gao et al., 2013; Jorm, 2001; Ownby et al.,
2006). It should be noted, however, that lack of associations have also been
reported (e.g., Becker et al., 2009; Lindsay et al., 2002).
Different outcomes from studies can, at least partly, be explained by
differences in the frequency and severity of depression (Byers & Yaffe, 2011).
Also, even if many studies have focused on late-life depression (>60 years of
age), it seems that depressive symptoms earlier in life also constitute a risk
factor. A recent longitudinal study found that depressive symptoms in
midlife as well as in late-life were associated with dementia (Barnes, Yaffe,
Byers, McCormick, Schaefer, & Whitmer; 2012). They also found that
recurrent depression seems to be associated with higher risk of VaD,
whereas depression that begins later in life can be part of the AD prodrome.
Sachs-Ericsson et al. (2005) found that depressive symptoms
predicted cognitive decline over a follow-up of three years among elderly
persons who had no cognitive impairment at baseline. Furthermore, Sawyer,
Corsentino, Sachs-Ericsson, and Steffens (2012) found that depression was
associated with a decrease in right hippocampal volume, and, therefore, they
suggested that there might be a causal relationship between depression and
cognitive decline. A number of other studies have found an association
between severe depression and decreased size of the hippocampus (Sheline,
Sanghavi, Mintun, & Gado, 1999; Sheline, Wang, Gado, Csernansky, &
Vannier, 1996; for exceptions, see: Ashtari et al., 1999; Rusch, Abercrombie,
Oakes, Schaefer, & Davidson, 2001).
Lifestyle
Smoking is undoubtedly negatively related to many health outcomes. In
recent years, interest has increased in terms of the relationship of smoking to
cognitive factors. It has been reported that smoking can be associated with
decreased risk of dementia (Almeida, Hulse, Lawrence, & Flicker, 2002), but
later meta-analyses, based on longitudinal studies, have found that smoking
overall is related to increased risk of cognitive decline, all-cause dementia,
VaD, and AD, although results are mixed (Anstey, von Sanden, Salim, &
O’Kearney, 2007; Cataldo, Prochaska, Glantz, 2010; Peters, Poulter, Warner,
Beckett, Burch, & Bulpitt, 2008). A history of heavy smoking (• 1 pack per
day) can be related to a 2–3 year earlier onset of AD (Harwood et al., 2009).
It should be noted, however, that nicotine has been suggested to have
positive short-term effects on cognition, with improved attention, and has
also been positively associated with short-term and long-term memory
performance (Murray & Abeles, 2002).
Another common lifestyle feature is alcohol consumption. Although it
has been found that long term and extensive alcohol consumption might
impair cognitive processing, and also result in so called alcohol-related
37
dementia (Mukamal et al., 2006; Oslin, Atkinson, Smith, & Hendrie, 1998),
meta-analyses of 15 prospective studies (with follow-ups of 2–8 years) by
Anstey, Mack, and Cherbuin (2009) investigating the relationships between
alcohol consumption and dementia and cognitive decline revealed that
relative risks were lower for all-cause dementia (and also for AD and VaD
separately) for light to moderate drinkers compared with non-drinkers. Also,
when classified more generally as drinkers versus non-drinkers, alcohol
consumption was associated with reduced risk for dementia and AD.
Remarkably, heavy drinking was not associated with increased risk. A
systematic review with meta-analyses on longitudinal studies (Peters, Peters,
Warner, Beckett, Bulpitt, 2008) exclusively among the elderly (•65 years)
confirmed that small amounts of alcohol can actually be protective against
all-cause dementia and AD, although associations were not significant for
VaD or cognitive decline.
A recent study by Sabia et al. (2014) investigated the association
between midlife (mean = 56 years) alcohol consumption and later risk of
cognitive decline over a follow-up of 10 years. The results indicated that
among men, excessive alcohol consumption was associated with more rapid
cognitive decline in all measures of cognition (global cognition, executive
functioning, and memory) compared with low or moderate consumption.
Among women, those who abstained from alcohol showed faster decline
than light or moderate drinkers, both in global cognition and in measures of
executive functioning. Although the results were different for men and
women, light to moderate alcohol consumption was not associated with risk
of cognitive decline, and this was similar to the results of studies on the
elderly.
Physical activity, another lifestyle factor, has been suggested to have
cognitive and neural benefits (Churchill et al., 2002), and several studies
have suggested that low physical activity increases the risk of cognitive
impairment and dementia (e.g., Barnes, Blackwell, Stone, Goldman, Hillier
& Yaffe, 2008; Laurin, Verreault, Lindsay, MacPherson, & Rockwood, 2001;
Weuve, Kang, Manson, Breteler, Frog Ware & Stein, 2004; Yoshitake et al.,
1995). It has, for example, been demonstrated that daily walking decreases
the risk of cognitive decline among elderly women (Barnes et al., 2008;
Weuve et al., 2004), and combining different forms of exercise appears to
provide a larger effect on cognition among the elderly than simply engaging
in unilateral training (Colcombe & Kramer, 2003).
It has also been suggested that exercise in midlife is associated with
reduced odds of dementia later in life (Rovio et al., 2005; Andel, Crowe,
Pedersen, Fratiglioni, Johansson, & Gatz, 2008). However, it should be
noted that there are studies finding no relationship between physical
activities and cognitive impairment (e.g., Wang et al., 2006) or dementia
(e.g., Verghese et al., 2003). One reason for different outcomes might be due
38
to the lack of studies that have a sufficient number of participants. In
addition, data are often based on different measures of subjective estimation
of activity, rather than having objective measures, and thus the results are
not sufficiently reliable (McAuley, Kramer & Colcombe 2003). Meta-analysis
based on nearly 134 studies examining either acute or long-term exercise
effects on cognition suggests that all together there is a small but positive
impact of exercise and fitness on cognitive performance. Physically fit people
seem to have a better ability to perform rapidly and more accurately on a
number of tasks measuring perception and cognition than the physically
unfit. Furthermore, exercise as a constant action to produce fitness, and/or
adopted by an individual for a long period of time, seems to have an even
larger impact on cognition (Etnier, Salazar, Landers, Petruzzello, Han &
Nowell, 1997).
Genetics
The study of genetics is a growing frontier in cognitive aging, and it is clear
that changes in cognitive functioning that come with aging can be influenced
by both genetic and environmental factors (Gatz, et al., 2006). One factor
associated with cognitive functioning is the Apolipoprotein E (APOE) gene
(Corder et al., 1993), a gene that also has been related to health and longevity
(Smith, 2002). APOE is a protein that, among other things, is involved in
cholesterol transport and the processes of neuronal repair (Mahley, 1988;
Rubinsztein, 1995). The gene coding for APOE is positioned on chromosome
19, and every individual holds two alleles, one from each parent. There are
three types of alleles—‫ܭ‬2, ‫ܭ‬3, and ‫ܭ‬4—which make six combinations possible
for each individual. The frequency of allele distribution differs among ethnic
populations, with the ‫ܭ‬3 allele most common in the general Swedish
population (Eggertsen, Tegelman, Ericsson, Angelin & Berglund, 1993).
Research suggests that if you are a carrier of the ‫ܭ‬4 allele you have an
increased risk of faster cognitive decline (Corder et al., 1993; Nilsson et al.,
2006) and increased risk of AD (Smith, 2002). A meta-analysis (Farrer et al.,
1997) showed that among carriers of one APOE-‫ܭ‬4 allele, the risk of
developing AD increased 3 to 4 times compared to a non-carrier, and among
those with two alleles the risk was 10 to 12 times higher. It has been
suggested, however, that with increased age the APOE-‫ܭ‬4 allele becomes less
efficient in predicting the risk for developing AD (Smith, 2002), although it
is still a potential risk factor later in life, particularly in homozygote carriers
of the ‫ܭ‬4 allele. It should also be noted that the ‫ܭ‬4 allele has been associated
with increased risk of other dementia diseases such as VaD (see Frisoni et al.,
1994; Stengard et al., 1995; Treves et al., 1996). In contrast to the risk
associated with APOE-‫ܭ‬4, APOE-‫ܭ‬2 has been suggested to be protective
(Corder et al., 1994).
39
Research has also revealed that ‫ܭ‬4-carriers, both middle-aged and
elderly, although still cognitively healthy, perform worse on several cognitive
tasks than non-carriers (e.g., Berr et al., 1996; De Blasi et al., 2009;
Greenwood, Lambert, Sunderland, & Parasuraman, 2005; Helkala et al.,
1995; Packard et al., 2007; Zehnder et al., 2009). Meta-analyses (Wisdom,
Callahan, & Hawkins, 2011; Small, Rosnick, Fratiglioni, & Backman, 2004)
confirm this pattern and demonstrate more pronounced differences on
measures of perceptual speed, executive functioning, and global cognition.
However, not all studies report an association between cognitive ability and
the ‫ܭ‬4 genotype (e.g., Bathum, Christiansen, Jeune, Vaupel, McGue, &
Christensen, 2006; Chen et al., 2002; Garcia et al., 2008; Kim et al., 2002;
Marquis et al., 2002; Salo et al., 2001).
The empirical studies
Main objectives
The overarching aim of this thesis was to longitudinally investigate the
influence of social relationships and leisure activity on the risk of future
dementia and age-related cognitive decline. The empirical studies conducted
as part of this thesis were motivated by the cognitive reserve hypothesis,
which assumes that mental stimulation is beneficial for cognitive functioning
and might postpone the age of onset of cognitive decline and dementia.
Specific aims:
Study I:
To investigate the association between participation in various leisure
activities in old age (•65 years) and risk of incident all-cause dementia.
Study II:
To investigate the association between various aspects of social relationships
in old age (•65 years) and risk of incident all-cause dementia and AD.
Study III:
To investigate the association between social network size and cognitive
ability in a middle-aged (40–60 years) sample.
40
Methods
The Betula Prospective Cohort Study
All three studies were based on data that emanated from the Betula
Prospective Cohort Study (Nilsson et al., 1997, 2004). The Betula study is a
longitudinal study on memory, aging, and health that started in Umeå,
Sweden, in 1988. The aims of the study were to investigate how health and
memory develop in adulthood and old age, to identify preclinical signs of
dementia, and to assess premorbid memory function. Participants were
selected from the population registry of Umeå using stratified (age, gender)
random sampling with a narrow-age cohort design (5 years between each age
cohort) and with a gender distribution representative of that in the Umeå
community. Inclusion in the study required a non-demented status at time
of entry, Swedish as a native language, and absence of severe visual or
auditory handicaps.
Since 1998, data have been collected on six test occasions (1988-1990,
1993-1995, 1998-2000, 2003-2005, 2008-2010, and 2013-2014). At each
occasion, participants were interviewed, tested cognitively, and examined
medically. Participants were invited to two sessions, about one week apart.
The first focused on a health assessment, and the second focused on an
extensive examination of memory. The health examination was performed
by a nurse, and testing of memory functions was performed by a trained
research assistant. In addition to measurements of cognition and health
(including information about life events, social situation, lifestyle, etc.), all
participants were followed over time and followed-up with regard to
dementia status by a clinical and research geriatric psychiatrist, and these
follow-ups currently cover the period from 1988 to 2010.
The design of the Betula study, including the age range for each
sample at inclusion, is presented in Table 1. The first sample (S1), assessed in
1998, included 1000 participants with 100 individuals in each age cohort (35
years, 40 years, 45 years, etc.). New samples have been included in the study,
and in most cases the goal was to recruit 100 participants for each age
cohort. Two new samples were included at test wave 2 (1993-95).
Participants of S2 were as old as S1 was at test wave 1, whereas S3 were the
same age as S1. So far about 4,500 individuals have participated in the
Betula study.
41
Table 1. The design of the Betula Study. A stratified random sampling is used (age, sex) that
corresponds to the distribution in Umeå.
Sample
Agerange*
T1
T2
T3
T4
T5
T6
(1988Ͳ1990) (1993Ͳ1995) (1998Ͳ2000) (2003Ͳ2005) (2008Ͳ2010) (2013Ͳ2014)
S1
S2
S3
S4
S5
S6
35Ͳ80
35Ͳ80
40Ͳ85
35Ͳ90
35Ͳ95
25Ͳ80
S1
S1
S1
S2
S2
S3
S3
S1
S1
S1
S3
S3
S3
S4
S5
S6
*Agerangeatinclusion
The Västerbotten Intervention Programme
Study III includes, in addition to data from the Betula study, information
collected as part of the Västerbotten Intervention Programme (VIP)
(Norberg et al., 2010). The VIP is an ongoing population-based intervention
that started in 1985 in Norsjö, Sweden. The main purpose of the VIP is to
reduce morbidity and mortality caused by cardiovascular diseases and
diabetes, because in Sweden, especially in the county of Västerbotten in the
north of Sweden, the number of mortality cases caused by cardiovascular
diseases increased steadily during the 20th century. The VIP was
subsequently implemented across Västerbotten, which also includes Umeå
municipality. Since 1995, all inhabitants of Västerbotten that are 40, 50, and
60 years old are invited to voluntarily participate in the study to get a risk
factor screening and to get individual counseling about healthy lifestyle
habits. About 6,500–7,000 examinations take place each year, and the VIP
includes data for almost 90,000 participants. Up to the year 2010, nearly
27,000 subjects had participated twice (Norberg et al., 2010).
The VIP is implemented in primary health care routines, and the
assessment is carried out in a single session at the participant’s health center
and includes a medical examination and a questionnaire including
information about health, lifestyle, social situation, etc. The medical
examination is performed by a nurse, and for each participant a health
profile is created that is discussed with the nurse. The purpose of this is to
encourage behavior that facilitates health, for instance, by changes in
lifestyle habits that decreases the risk of heart disease, diabetes, stroke, etc.
The Linnaeus Database
Information from the Betula project and the VIP is linked within the
Linnaeus database (Malmberg, Nilsson, & Weinehall, 2010). The database is
42
an anonymized longitudinal database that was established in 2009, and it is
designed to provide the opportunity for interdisciplinary research. The
database has been developed at the Centre for Population Studies at Umeå
University and includes information from several sources of Swedish register
data. In addition to data from the Betula study and the VIP, the Linnaeus
database contains information from Statistics Sweden and the National
Board for Health and Welfare, databases that, for example, provide
information about causes of death, hospitalization, and socio-economic
conditions. The database is designed for both cross-sectional and
longitudinal studies and provides the opportunity to examine relationships
between health status, cognitive function, lifestyle, various health indicators,
socioeconomic conditions, population dynamics, etc. Data from Betula are
available in the database up to T4 (2003-2005), and most of the cognitive
data are included. Data regarding genetics and dementia collected within
Betula, however, are not included in the Linnaeus database.
Measures
Dementia and AD (Studies I & II). Dementia status is available on an
annual basis for most of the participants in the Betula project. The currently
available information about dementia status covers the years 1989 to 2010.
Diagnoses were established in accordance with the criteria of the DSM-IV
(American Psychiatric Association, 2000). The analyses were conducted by a
clinical research and geriatric psychiatrist who performed several
prospective and retrospective analyses of the information that was available
at each test wave. This included comprehensive analysis of various general
and specific health measures, results from neuropsychological testing, and
clinical course. In addition, data have been blindly assessed to increase the
validity of cognitive status. The clinical course over time is of importance
when establishing diagnoses, and thus, in 2010, the data were re-analyzed
and clinical course were related to the cognitive status at the inclusion of the
study. Incorrectly included participants (T1-T5) accounted for only 0.4% of
all the participants, which indicates a high validity of dementia diagnosis. In
Studies I and II, when using dementia as an outcome variable, all types of
dementia were used, including AD, VaD, DLB, FTD, PDD, and NUD. For
Studies I and II, AD was also used as a dependent variable. Dementia and
AD were coded in the same way (coded as 1) in both studies when compared
to the non-demented (coded as 0).
Episodic memory (Study III). The following episodic memory measures
from the Betula study were included in Study III (for a detailed description,
see Rönnlund & Nilsson, 2006b).
43
x
Recall of sentences: the participants were shown sixteen written
sentences, each for eight seconds. The sentences were also read
aloud by the experimenter. This was followed by free recall of as
many sentences as possible directly after.
x Recall of actions: A total of 16 different actions were performed with
different objects, and each action was performed for eight seconds.
This was followed by free recall directly after.
x Category cued recall: Following the free recall tasks described above,
participants were asked to recall as many nouns as possible
presented either among sentences or actions. Eight semantic
categories presented on a sheet served as cues for the recollection.
x Recall of nouns: A list of 12 nouns was presented at a pace of 2
seconds per item, and participants were asked to recall as many
nouns as possible in any order at the same rate (2 seconds per item).
x Activity recall: At the end of the test session, the participants were
asked to recall as many of the tasks as possible, in any order, that
they had performed during the test session. The total number of
recalled tasks served as the score for the test.
A unit-weighted (z) episodic memory composite score was computed based
on the scores for each measure. The test-retest (stability) coefficients for the
composite score were r = .75 (p < .01) and r = .73 (p < .01) for 5-year and 10year follow-ups, respectively.
Semantic memory (Study III). Four individual measures were used as
indicators of semantic memory (cf. Nyberg et al., 2003). Three of them were
verbal fluency tasks requiring the participants to generate as many words as
possible during one minute. The restrictions were as follows: generate words
with the initial letter A (not names), words beginning with the letter M and
containing five letters (not names), and professions beginning with the letter
B. The fourth task used to measure semantic memory was the SRB, a 30item multiple-choice synonym test (Dureman, 1960) in which the
participants are asked to underline the correct synonym of each target word.
Each target has five alternatives. The time limit for the task is seven minutes,
and the number of correctly identified synonyms is used as the score for the
test. A semantic composite (z) score was computed based on the four
indicators. The stability coefficient for the semantic composite score was r =
.785 (p < .01) for the 5-year follow-up and r = .751 (p < .01) for the 10-year
follow-up.
Visuospatial ability (Study III). The WAIS-R Block Design Test
(Wechsler, 1981) was used as a measure of visuospatial ability. The task
requires a set of four or nine bicolored blocks. The participants are required
to use the blocks to duplicate a maximum of nine target patterns presented
44
in order of ascending difficulty. A raw score is generated based on the
number of trials and time to complete the patterns. The scores for the
present purposes were transformed to z-scores. Cronbach’s Į of .82 has been
reported for this test in a Betula sample (Rönnlund & Nilsson, 2006a). The
stability coefficient for visuospatial score was r = .78 (p < .01) for the 5 year
follow-up and r = .76 (p < .01) for the 10-year follow-up.
Leisure activities (Study I). In a questionnaire, which was sent to the
participants about a week prior to their health examination for the Betula
study, the participants indicated how often they engaged in 16 different
leisure activities during the past three months. The activities were
Travel/Trips; Sports/Exercise/Walking in the forest; Hunting/Fishing;
Reading books; Reading magazines; Reading newspapers; Watching
TV/Listening to the radio; Movies/Concerts/Theater; Restaurant visits;
Spending time with family, relatives, and friends; Attending
courses/workshops; Religious assemblies; Association work; Playing musical
instruments; Needlework; and Other Hobbies/Activities.
The participants rated their activity frequency level on an ordinal scale
ranging from never (coded as 0), occasionally (1), a few times a month (2),
sometime per week (3), or every day (4). To obtain a meaningful number of
observations, however, a new classification scale was made ranging from
never (coded as 0), occasionally or a few times a month (1), and sometime
every week or every day (2). Of the participants, 97.1% reported that they
read the newspaper every day, and 98.3% that they watched TV/listened to
the radio daily. Due to extreme skew, these two questions were not recoded
and were excluded from further analyses.
Using these new classifications, a "total" index (maximum 28 points)
was computed based on the sum score of all 14 activities. To investigate
whether different dimensions of leisure activities are associated with
incident dementia, participants from Sample 5 (M = 67 years) of the Betula
study were asked to estimate if an activity was predominantly “mental,”
“social,” or “physical.” A questionnaire was sent out where participants rated
every activity on a scale from 1 to 10 for each of these three dimensions.
Based on responses from 18 participants, two indexes were computed. The
"mental"
index
included
Read
books;
Read
magazines;
Movies/Concerts/Theater; Play musical instruments; Needlework; and
Hunting/Fishing (max 12 points), and the "social" index included
Travel/Trips; Restaurant visits; Spend time with family, relatives, and
friends; Attending courses/workshops; Religious assemblies; and
Association
work
(max
12
points).
Only
one
activity,
Sports/Exercise/Walking in the forest, was regarded as predominantly
physical. Thus, no index was generated to represent physical activity.
45
Cronbach's Į for the Social Index was .49, for the Mental Index .23, and for
the total index .59.
Social relationships (Study II). Data from five questions about social
relationships collected in the Betula study were used: (1) Living status
(Living with Wife/Husband/Common law spouse/Children/Sibling or Other
person = (coded as) 1/, living alone = 0), (2) “Do you have any really close
friends whom you can contact and talk to about anything?” (yes = 1/no = 0),
(3) “Do you think you meet your friends and acquaintances often enough?”
(yes = 1/no = 0), (4) “How often do you visit or are visited by your friends
and acquaintances?” (Daily/Several times a week/Once a week/Between
once a week and once a month/Between once a month and once a
quarter/Less than once a quarter/Never), and (5) “How often do you have
contact with your friends and acquaintances in other ways than visits?”, for
example, by telephone (Daily/ Several times a week/Once a week/Between
once a week and once a month/Between once a month and once a
quarter/Less than once a quarter/Never). To obtain a meaningful number of
observations regarding the two latter questions about visits and contact
frequency, a new classification was made into once a week or more, (coded
as 1), and less than once a week (0). Finally, an index was computed as the
sum of scores for the entire set of relationship variables (maximum 5 points)
Social network size (Study III). Four questions collected in the VIP as
part of a questionnaire designed to measure social relationships were used as
indicators of social network size. The questions were: (1) “How many
persons do you know and have contact with that have the same interests as
you?”, (2) “How many persons, that you know, do you meet or talk with
during a regular week?”, (3) “How many friends do you have that can come
into your home at any time and feel at home?”, and (4) “How many are there,
in your family and among your friends, with whom you can talk freely
without reflection?” For each of the questions, the participants were
requested to indicate the relevant number on a scale having the following
alternatives: nobody (coded as 0), 1-2 persons (1), 3-5 persons (2), 6-10
persons (3), 11-15 persons (4), and more than 15 persons (5). An index for
network size was computed as the sum score of the four questions (max =
20; Cronbach's Į = .77)
Different covariates were used for the three studies as shown in Table 2. For
Study III, which used data from Betula and VIP linked in the Linnaeus
database, all the information collected in Betula is not available.
46
Table 2. Variables used as covariates in the different studies included in this thesis.
Factors
Age
Gender
Yearsofeducation
Maritalstatus
Cardiovascularriskfactors
Subjectivehealth
Smoking
Alcoholconsumption
Physicalactivity
Obesity
Stress
Depressivesymptoms
Genetics
Globalcognitivestatus
StudyI
X
X
X
X
X
StudyII
X
X
X
StudyIII
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Note. Detaileddescriptionforeachvariableisinthemaintext
Potential confounders (source in brackets):
x
Age (Betula).
x
Gender (Betula).
x
Years of education (Betula).
x
Marital status (Betula). Coded as four different categories:
married-cohabiting/unmarried/divorced/ or widow-widower.
x
Cardiovascular risk factors (Betula). A sum of four self-reported
diseases/conditions was used: high blood pressure (yes/no), stroke
(yes/no), incidence of heart attack/cardiovascular problems
(yes/no), and diabetes (yes/no).
x
Subjective health (VIP). Rating of health status during the last
year, ranging from “very good”, “quite good”, “reasonably”, “rather
poor”, to “poor.” For the analyses, rather poor and poor were coded
as 0, and reasonably, quite good, and very good were coded as 1.
47
x
Smoking (Betula). Do you smoke, or used to, practically daily?
(yes/no).
x
Alcohol consumption (Betula). In Studies I and II, three
categories were used: currently drink, never drank, or, have quit
drinking. In Study III, two categories were used: currently drink, or
never drank - have quit drinking.
x
Physical activity (VIP). Participants rated frequency of
participation in exercise “within the last three months, wearing an
exercise outfit, with the purpose of improving physical status or to
feel good” on a scale ranging from never, now and then but not
regularly, once a week, 2–3 times per week, or more than 3 times a
week. A dummy variable was generated as a measure of low activity
(never, now and then but not regularly) and high activity (once a
week or more).
x
Obesity (Betula). BMI • 30 was treated as obesity and was
compared to BMI <30.
x
Stress (Betula). The question was asked, “Do you in general feel
stressed?” (yes/no).
x
Depressive symptoms (Betula). In Study I: Feelings of depression
(yes/no). In Studies II and III: Index of depressive symptoms,
calculated by the sum of six self-reported measures: (1) often feel
lonely, (2) often feel dispirited, (3) feeling anxious, (4) loss of
appetite, (5) difficulty sleeping, and (6) fatigue.
x
Genetics (Betula). APOE genotyping was performed by using
polymerase chain reaction. The APOE genotype was categorized
based on the presence of at least one ‫ܭ‬4 allele as compared to noncarriers. The ‫ܭ‬2/‫ܭ‬4 combination was not included in the analyses
because it has been suggested that the ‫ܭ‬2 allele might be protective
(Corder et al., 1994).
x
Global cognitive status (Betula). Mini Mental State Examination
(MMSE, Folstein et al., 1975) was used as an estimation of global
cognitive status. The measure was originally developed for screening
and identification of dementia, not as a diagnostic test, but rather as
an indicator of cognitive status.
48
Summary of Study I
Sörman, D. E., Sundström, A., Rönnlund, M., Adolfsson, R., & Nilsson, L.-G.
(2014). Leisure activity in old age and risk of dementia: a 15-year
prospective study. Journals of Gerontology, Series B: Psychological
Sciences and Social Sciences, 69, 493–501.
Aim
To investigate the association between participation in various leisure
activities in old age (•65 years) and the risk of incident dementia.
Participants
The number of participants aged 65 years or older at baseline was 1,812.
Participants who had not answered the questionnaire about leisure activities
(n = 58), who were lost at follow-up (n = 41), had dementia at inclusion (n =
19), who died during the first five years after baseline (n = 209), and a few
subjects diagnosed with dementia shortly after the last follow-up
examination in 2010 (n = 10) were excluded. Thus, a total of 1,475 elderly
(836 females, 639 males) 65 years or older (M = 73.70 years, SD = 6.85
years) were included in the analyses. The participants belonged to Sample 1
(n = 314), Sample 2 (n = 344), Sample 3 (n = 383), Sample 4 (n = 217), and
Sample 5 (n = 217) in the Betula study.
The relatively large number of unanswered questions about leisure
activities on the questionnaire was because the participants had to fill it out
at home. Within this group of participants were individuals from Sample 1
which had become demented at the time of T2 (baseline for Sample 1 in this
study). The reason for excluding participants who had died (without having
developed dementia) 1–5 years after baseline was to minimize the risk of
including non-demented persons who were less active as a consequence of
poor health, and thus reducing the plausible effects that an active lifestyle
might have on cognition. The reason to exclude those who were lost to
follow-up (for example, those who moved outside the catchment area) was to
ensure that all participants could be regarded as either demented or nondemented over the entire follow-up period. For this study, participants who
developed dementia shortly after the last assessment (2010) were excluded.
This decision was made on the assumption that it would be incorrect to
consider people who became demented shortly after the study end as nondemented in the analyses, although the end-date for this study was set at the
end of the last follow-up examination.
The fact that 65 years was chosen as baseline age was partly
motivated by the fact that 65 is the most common retirement age in Sweden.
This gives more similar opportunities for engagement in leisure activities.
This age is also interesting because it is around this age (age 60–65) that
49
episodic memory functioning, in general, begins to deteriorate (Rönnlund et
al., 2005).
Statistical procedure
Because questions about leisure activities were included in the Betula project
at T2 (1993-95), and because some samples (S4, S5) were first included at
later test occasions, it was necessary to adapt the design of this study to
account for this. For Sample 1, which participated at T1 (1988–1990) leisure
activity data were available first at T2, whereas for Samples 2 and 3 (T2,
1993–1995), Sample 4 (T3, 1998–2000), and Sample 5 (T4, 2003–2005),
the questionnaire was answered the first time individuals participated in the
study. Because the time when answering questions regarding leisure
activities for the first time differed depending on sampling, the data were
adjusted to get a common baseline for all participants (i.e. the first time
participants answered the questionnaire) to be able to include as many
participants as possible in the study. The design of Study 1 is presented in
Figure 2.
Figure 2. Design of the analyses over three time periods. Those having dementia within every
time period were compared with those without dementia. The number of cases with dementia is
shown in the boxes.
50
Although the returnee rate in the Betula study is high in general (7886%), not all samples return (due to design), and thus longitudinal data are
not available for all samples regarding health, leisure, and cognitive
measures. Longitudinal follow-up regarding dementia status is, however,
available for all samples. The long follow-up made it possible to examine the
longitudinal effects of leisure, not only over the total follow-up period, but
also 1–5 years, 6–10 years, and 11–15 years after baseline to see whether or
not effects differed between the various time intervals and in order to take
into account whether or not reverse causation could confound the results.
Samples 1–3 could be included in analyses of three intervals (1–5 years, 6–10
years, and 11–15 years). Sample 4 was included in two intervals (1–5 years
and 6–10 years), and Sample 5 was included in one interval (1–5 years).
Cox proportional hazard regressions were used in the analyses, and
time-to-event was calculated from date of health assessment to the year of
(1) dementia diagnosis, (2) death, or (3) date of the end of follow-up (2010).
Survival time was rounded down to the nearest whole year. For example, an
individual in Sample 5 could have a survival of almost six years, but this was
then rounded down to 5 years. This also corresponds well to the test intervals
used in the Betula project (every fifth year). Participants who developed
dementia during any of the time intervals were never used as healthy
controls in the analysis of other time intervals. However, subjects who never
developed dementia could serve as healthy controls in analyses of several
time periods. Furthermore, participants who died during any time period
were not used as controls in the analysis of that period. Hence, the analyses
of each period consisted only of participants who either became demented or
those who survived the entire period without having dementia.
Results
Over the total follow-up period, 357 incident dementia cases were identified.
Among those who received a diagnosis of dementia were 162 diagnosed 1–5
years after baseline, 139 participants 6–10 years after baseline, and 56 cases
that were identified during the last time-period, 11–15 years after baseline.
Over the same periods, there remained 1,118 (1–5 years), 682 (6–10 years),
and 404 (11–15 years) participants who were dementia free. Overall, those
who developed dementia were, compared with those who remained without
dementia, older, females, less often married, less frequent users of alcohol,
and carriers of the APOE ‫ܭ‬4 allele.
Results from the Cox proportion regression analyses investigating the
associations between leisure activities and dementia are presented in Table
3.
51
Table 3. Associations between the activity indexes and dementia presented as hazard ratio
(HR).
Activity
FirstTimeͲperiod
(n =1280)
HR
CI(95%)
SecondTimeͲperiod
(n =821)
HR
CI(95%)
ThirdTimeͲperiod
(n =460)
HR
CI(95%)
TotalTimeͲperiod
(n =1475)
HR
CI(95%)
Model1
MentalActivityIndex
SocialActivityIndex
TotalActivityIndex
0.95
0.92*
0.95*
0.86Ͳ1.03
0.84Ͳ0.99
0.91Ͳ0.99
0.94
0.99
0.97
0.85Ͳ1.03
0.91Ͳ1.09
0.93Ͳ1.07
1.06
0.95
1.01
0.90Ͳ1.24
0.82Ͳ1.09
0.94Ͳ1.10
0.95
0.95
0.96*
0.89Ͳ1.01
0.90Ͳ1.00
0.94Ͳ0.99
Model2
MentalActivityIndex
SocialActivityIndex
TotalActivityIndex
0.96
0.90*
0.95*
0.88Ͳ1.05
0.82Ͳ0.98
0.91Ͳ0.99
0.94
0.99
0.97
0.85Ͳ1.03
0.91Ͳ1.09
0.93Ͳ1.02
1.06
0.98
1.03
0.89Ͳ1.25
0.84Ͳ1.14
0.95Ͳ1.12
0.95
0.95
0.97*
0.90Ͳ1.01
0.89Ͳ1.00
0.94Ͳ0.99
Model3#
MentalActivityIndex
SocialActivityIndex
TotalActivityIndex
(n =826)
0.94
0.85Ͳ1.05
0.86*
0.77Ͳ0.96
0.93*
0.88Ͳ0.98
(n =619)
0.93
0.83Ͳ1.04
1.00
0.91Ͳ1.10
0.97
0.92Ͳ1.02
(n =418)
1.10
0.92Ͳ1.30
0.99
0.84Ͳ1.17
1.04
0.95Ͳ1.14
(n =996)
0.95
0.88Ͳ1.02
0.94*
0.88Ͳ1.00
0.96*
0.93Ͳ0.99
Model1.Adjustingforage,gender,andeducation
Model2.Adjustingforage,gender,education,diseases,smoking,alcoholuse,maritalstatus,generalstress,andfeelingsofdepression
Model3.Adjustingforage,gender,education,diseases,smoking,alcoholuse,maritalstatus,generalstress,feelingsofdepression,andAPOEgenotype
#
LessparticipantsduetomissingdataonAPOE
*P <.05
After controlling for all potential confounders (age, gender, education,
diseases, smoking, alcohol use, marital status, general stress, feelings of
depression, and APOE genotype), the results from the analyses of the total
time period showed that higher levels in the “Social” and “Total” activity
indexes were associated with decreased risk of dementia in old age. The
“Mental activity” index was not associated with decreased risk of dementia in
old age. Analyses of the separate time periods after baseline (1–5 years, 6–10
years, and 11–15 years), however, only revealed significant associations for
these indexes in the first time period (1–5 years) after baseline. Analyses
combining the second and third periods after baseline did not alter the
results. Thus, results from this study provide little support that engagement
in leisure activities protects against dementia across a longer time frame.
Effects found for the first time period might indicate positive short-term
effects, but might also reflect reverse causality, suggesting that that the
preclinical phase of dementia influences the level of engagement in leisure
activities. It should be noted that potential associations depending on APOE
status were also examined. In addition, separate analyses with AD as the
outcome were also performed. These analyses, however, did not change the
pattern of the results. Although not presented in the article of study I, results
showed that higher age and being an ‫ܭ‬4-carrier were factors that had the
strongest negative associations with all-cause dementia and AD across a
longer time frame. In some models, marital status (widowed, but not single
or divorced) turned out to be a risk factor for dementia, whereas female
gender was a risk factor for AD.
52
Summary of Study II
Sörman, D. E., Rönnlund, M., Sundström, A., Adolfsson, R., & Nilsson, L.-G.
(2015). Social relationships and risk of dementia: a population-based
study. International Psychogeriatrics. Advance online publication.
Aim
To investigate the association between various aspects of social relationships
in old age (•65 years) and risk of incident all-cause dementia and AD.
Participants
A total of 1,769 non-demented participants took part in a health assessment
at baseline, and this was when questions about social relationships were
asked. Participants who did not answer the questions about social
relationships (n = 19), had missing data on the MMSE (n = 2) and obesity (n
= 10), or who had a survival time of less than one year after health
assessment (n = 23) were excluded. Thus, the final sample consisted of 1,715
participants (958 women, 757 men) who were 65 years or older (M = 74.20
years, SD = 7.01 years). Participants were from Sample 1 (n = 382), Sample 2
(n = 382), Sample 3 (n = 448), Sample 4 (n = 249), and Sample 5 (n = 254)
of the Betula study. The main reason to use 65 years as baseline age was the
same as in Study I.
For this study, it was decided to include individuals who were
diagnosed with dementia shortly after the study end (n = 10) and
participants who were lost to follow-up. The year of this event was set as the
end date when survival time was calculated. Although the actual outcome
over the whole follow-up period was unknown for those lost to follow-up, in
comparison to other participants, it was decided that it could be relevant to
include them based on the time they participated. Time to event for each
individual was calculated from date of health assessment to the year of (1)
dementia diagnosis, (2) lost to follow-up, (3) death, or (4) the end of followup.
Statistical procedure
Because questions about social relationships were included in the Betula
study at T2 (1993–95), and because some samples (S4, S5) were first
included at later test occasions, the data were adjusted to get a common
baseline (similar to Study I). The design of the study is presented in Figure 3.
53
Figure 3. The design of Study II. All cases of dementia, as well as AD separately, were compared
to the non-demented in the analyses over the total follow-up period. Analyses were also
performed with delayed entry as indicated by blue arrows.
In addition to analyses of the total follow-up period after baseline
measurement (up to 16 years), analyses were performed with delayed entry.
In these analyses, participants with short survival time were excluded. This
was performed to adjust for reverse causality and to isolate long-term effects.
The results from Study I, and limitations regarding directionality in previous
studies (see Pillai & Verghese, 2009) motivated this.
Cox proportional hazard regressions were used to investigate possible
associations. In analyses with delayed entry, entry time after assessment of
social relations was moved forward stepwise by one year at a time. Because
only participants with at least one survival year were included in this study,
the first time point of delayed entry was set at 2 years.
Results
Over the total follow-up period (16 years), 373 incident cases of dementia
were identified, including 207 cases of AD. Concerning baseline
characteristics, those who developed dementia and AD were more often of
female gender, did not drink alcohol, lived alone, and had lower mean values
on both the MMSE and on the relationship index. In addition, participants
with all-cause dementia were older at baseline compared to those without
dementia, whereas obesity was less common among participants with
54
incident AD. Also, AD cases had a lower frequency of cardiovascular risk
factors at the group level in comparison with controls. The results from the
Cox proportion regression analyses investigating associations between social
relationships (living status, contact and visit frequency, satisfaction with
contact frequency, having/not having a close friend, and social relationship
index) in old age and risk of dementia and AD are presented in Tables 4 and
5 respectively.
Table 4. Associations between social relationship variables and all-cause dementia presented
as hazard ratios (HR).
ALLͲCAUSEDEMENTIA
ALLͲCAUSEDEMENTIA
Totalperiod(n =1715)
ш2years(n =1634)
HR(95%CI)
pͲVALUE
HR(95%CI)
pͲVALUE
ALLͲCAUSEDEMENTIA
ш3years(n =1545)
HR(95%CI)
pͲVALUE
Livingwithsomeone
No
Referencelevel
Ͳ
Referencelevel
Ͳ
Referencelevel
Ͳ
Yes
0.83(0.65Ͳ1.04)
0.103
0.80(0.63Ͳ1.02)
0.065
0.82(0.63Ͳ1.05)
0.111
Havingaclosefriend
No
Referencelevel
Ͳ
Referencelevel
Ͳ
Referencelevel
Ͳ
Yes
0.82(0.60Ͳ1.11)
0.199
0.85(0.61Ͳ1.17)
0.319
0.84(0.57Ͳ1.18)
0.306
Meetfriendsandacquaintan
No
Referencelevel
Ͳ
Referencelevel
Ͳ
Referencelevel
Ͳ
Yes
0.99(0.72Ͳ1.37)
0.957
1.02(0.73Ͳ1.44)
0.900
1.18(0.80Ͳ1.71)
0.406
Lessthanonceaweek
Referencelevel
Ͳ
Referencelevel
Ͳ
Referencelevel
Ͳ
Onceaweekormore
0.78(0.61Ͳ0.99)
0.037
0.82(0.64Ͳ1.05)
0.119
0.84(0.65Ͳ1.09)
0.195
Visiting/Visitsoffriendsandacquaintances
Contactwithfriendsandacquaintances
Lessthanonceaweek
Referencelevel
Ͳ
Referencelevel
Ͳ
Referencelevel
Ͳ
Onceaweekormore
0.94(0.68Ͳ1.30)
0.710
0.96(0.68Ͳ1.35)
0.804
0.91(0.64Ͳ1.30)
0.614
RelationshipIndex
0.88(0.79Ͳ0.98)
0.019
0.89(0.79Ͳ0.99)
0.044
0.91(0.80Ͳ1.02)
0.104
All analyses adjusted for age, gender, years of education, MMSE, cardiovascular risk fac tors, obesity, alc ohol use, smoking status, stress, and depressive symptoms.
55
Table 5. Associations between social relationship variables and AD presented as hazard ratios
(HR).
ALZHEIMER’SDISEASE
ALZHEIMER’SDISEASE
Totalperiod(n =1549)
ш2years(n =1480)
HR(95%CI)
pͲVALUE
HR(95%CI)
pͲVALUE
ALZHEIMER’SDISEASE
ш3years(n =1409)
HR(95%CI)
pͲVALUE
Livingwithsomeone
No
Referencelevel
Ͳ
Referencelevel
Ͳ
Referencelevel
Ͳ
Yes
0.80(0.59Ͳ1.09)
0.161
0.75(0.55Ͳ1.03)
0.080
0.74(0.53Ͳ1.03)
0.076
Havingaclosefriend
No
Referencelevel
Ͳ
Referencelevel
Ͳ
Referencelevel
Ͳ
Yes
0.72(0.48Ͳ1.07)
0.105
0.77(0.50Ͳ1.17)
0.214
0.77(0.50Ͳ1.18)
0.235
Meetfriendsandacquaintan
No
Referencelevel
Ͳ
Referencelevel
Ͳ
Referencelevel
Ͳ
Yes
0.93(0.61Ͳ1.42)
0.735
0.92(0.59Ͳ1.43)
0.709
1.00(0.63Ͳ1.60)
0.944
Visiting/Visitsoffriendsandacquaintances
Lessthanonceaweek
Referencelevel
Ͳ
Referencelevel
Ͳ
Referencelevel
Ͳ
Onceaweekormore
0.78(0.56Ͳ1.07)
0.127
0.85(0.61Ͳ1.20)
0.358
0.87(0.61Ͳ1.24)
0.431
Contactwithfriendsandacquaintances
Lessthanonceaweek
Referencelevel
Ͳ
Referencelevel
Ͳ
Referencelevel
Ͳ
Onceaweekormore
0.92(0.59Ͳ1.45)
0.719
0.99(0.61Ͳ1.61)
0.973
1.00(0.61Ͳ1.64)
0.991
RelationshipIndex
0.85(0.73Ͳ0.98)
0.025
0.86(0.74Ͳ1.00)
0.056
0.87(0.74Ͳ1.02)
0.090
All analyses adjusted for age, gender, years of education, MMSE, cardiovascular risk fac tors, obesity, alc ohol use, smoking status, stress, and depressive symptoms.
The results from the analyses of the entire follow-up period revealed
that higher values on the relationship index were associated with reduced
risks of both dementia and AD, a result that persisted after controlling for all
potential covariates (age, gender, years of education, cardiovascular risk
factors, obesity, alcohol use, smoking status, stress, and depressive
symptoms). Furthermore, visiting/visits of friends and acquaintances more
than once a week was related to decreased risk for all-cause dementia but not
for AD.
In the analyses with a delayed entry set to 2 years, 81 participants
were excluded with too short survival time (cases of all-cause dementia = 27,
without dementia = 54) resulting in a sample of 1,634 individuals. From the
sample (n = 1,549) investigating the association with AD, 69 participants
(AD = 15, without AD = 54) were excluded resulting in a sample of 1,480
individuals. The results revealed that the social relationship index was the
only variable that was associated with dementia (only all-cause dementia).
When entry was set to 3 years, 89 participants (cases of all-cause dementia
= 29, without dementia = 60) had to be excluded resulting in a sample of
1,545 participants. In analyses exclusively with AD, 71 participants were
additionally excluded (AD = 11, without AD = 60) resulting in a sample of
1,409 individuals. In these analyses, none of the indicators were significant
for either all-cause dementia or AD. Thus, although the results from this
56
study show that certain aspects of social relationships can be associated with
incident dementia or AD, it is plausible that these associations reflect reverse
causality.
Although not presented in the article for Study II, analyses including
only participants with relatively long survival time (•5 years) after baseline
before any event occurred showed that higher age and being an ‫ܭ‬4-carrier
(separate analyses including APOE data were performed) were the strongest
risk factors for all-cause dementia and AD across a longer time frame,
although it should be noted that the number of participants significantly
decreased in the analyses Furthermore, female gender was associated with
higher risk of AD, whereas obesity (BMI • 30) was associated with reduced
risk of both all-cause dementia and AD.
Summary of Study III
Sörman, D. E., Rönnlund, M., Sundström, A., Norberg, M., & Nilsson, L.-G.
(2015). Social network size and cognitive functioning in middle-aged
adults: Cross-sectional and longitudinal associations. Manuscript.
Aim
To investigate the association between social network size and cognitive
ability in a middle-aged (40–60 years) sample.
Participants
In study III, all of the participants had taken part in both the Betula study
and the VIP. The Linnaeus database was examined because participants
from Betula and VIP are linked within this database at the individual level.
The age range was restricted to 40–60 years due to the design of the VIP in
which participants are tested at 40, 50, and 60 years of age. Data from T2
(1993–1995), T3 (1998–2000), and T4 (2003–2005) in Betula were used
because information on cognitive function (Betula) and social network size
(VIP) were linked across these periods. A selection criterion was
participation in the two studies within a span of 12 months. The first time
fulfilling this criteria during any test occasion constituted the baseline for the
participant. The final sample was obtained after excluding individuals with
incomplete data on social network size (n = 14), cognition (n = 4), education
(n = 12), physical exercise (n = 3), subjective health (n = 4), or use of alcohol
(n = 1). Thus, a total of 804 middle-aged participants (448 females, 356
males) from Samples 1–3 who were 40–60 years old (M = 52.17 years, SD =
7.59 years) at baseline were included in the study.
57
Statistical procedure
Data were adjusted to get a baseline, including participants who were tested
in the two studies within a span of 12 months. The design and flowchart of
Study III is presented in Figure 4.
Figure 4. The design and flowchart of the study. Participants were tested every fifth year in the
Betula study and every tenth year in VIP.
aDue
to the study design, some participants from Sample 3 in the Betula study were not re-tested.
bDue
to the study design, participants from Sample 3 in the Betula study were not re-tested.
cDropouts
dFor
are participants who were deceased or did not want to participate at the follow-up measurement.
some participants (n = 131), follow-up information regarding social network size was also available.
For a number of participants, 5-year (n = 604) and 10-year (n = 255)
cognitive follow-ups were available. For some subjects (n = 131), 10-year
follow-up information on both social network size and cognitive functioning
was available (within a span of 12 months). This allowed for both crosssectional and longitudinal analyses of the effects of network size on cognitive
performance, although the number of individuals decreased considerably
with time.
Hierarchical multiple regression analyses were employed to examine
the relationship between network size and cognitive functioning. The
58
hierarchical entry of variables was used to sort out the influence of other
independent variables (demographic, health, and lifestyle factors) before
assessing the contribution of network size on measures of cognitive
functioning (episodic memory, semantic memory, and visuospatial ability).
This was found to be suitable because the independent variables correlated
with each other to a varying degree and also correlated with the cognitive
measures. Cross-lagged panel correlations were used to investigate the
directionality between network size and cognitive functioning among those
participants (n=131) with longitudinal data for both of these factors.
Results
Dropout analysis revealed that the 604 returnees at the 5-year follow-up did
not differ significantly in regard to background characteristics compared to
the non-returnees (n = 200), except that the returnees had a lower mean
score on the depressive symptom index. The 255 participants who were reassessed at the 10-year follow-up were younger and exhibited fewer
depressive symptoms than non-returnees (n = 549).
The results from the hierarchical multiple regression analyses
investigating associations between social network size and three cognitive
abilities (episodic memory, semantic memory, and visuospatial ability) are
summarized in Table 6.
Table 6. Results from analyses investigating associations between social network size and
cognitive functioning.
EpisodicMemory
Predictor
ȴR
2
SemanticMemory
ɴ
ȴR
2
ɴ
VisuospatialAbility
ȴR
2
ɴ
CrossͲsectional(n=804)
a
.005*
Step3
NetworkSize
TotalR
.009**
.074*
2
.007*
.099**
.298***
.275***
.088*
.168***
FiveYearFollowͲup(n=604)
b
Step4
.002
c
NetworkSize
TotalR
.003*
.046
2
c
.002
.058*
.603***
.635***
.047
.645***
TenYearFollowͲup(n=255)
b
.007*
Step4
NetworkSize
TotalR
.010*
.088*
2
.001
.104*
.588***
.592***
.032
.629***
a
Step1controlledforage,gender,andeducation.Step2controlledforsubjectivehealth,depressivesymptoms,physicalexercise,andalcoholuse.
b
Step1controlledforcognitiveperformanceatbaseline.Step2controlledforage,gender,andeducation.Step3controlledforsubjectivehealth,
depressivesymptoms,physicalexercise,andalcoholuse.
2
ȴR :RSquareChange,ɴ:StandardizedBeta.
c
*p <.05,**p <.01,***p <.001, p =.088
59
Results from the cross-sectional analyses demonstrated a positive
association between social network size and cognitive performance, and this
association was observed across the three cognitive abilities. In contrast, for
time-related changes the baseline network size was only positively related to
5-year changes in semantic memory, although almost reaching significance
for episodic memory. At the 10-year follow-up, the baseline network size was
associated with changes in both semantic and episodic memory but was still
unrelated to changes in visuospatial performance. Other factors associated
with cognitive change above baseline cognitive performance were age (5-year
change in episodic memory and visuospatial ability and 10-year change in all
constructs), female gender (5-year and 10-year change in episodic memory),
education (5-year change in all constructs), and physical exercise (5-year
change in semantic memory).
For a small portion of participants (n = 131), 10-year follow-up data
were available for network size. Cross-lagged panel correlations showed that
baseline network size was associated with future performance in semantic
memory as well as episodic memory. None of the abilities, however, were
related to future network size. Thus, the results from this study provide
support for a positive relationship between social network size and future
performance in declarative memory abilities. The results are in accordance
with the cognitive reserve model (Stern, 2002), suggesting that higher levels
of cognitive stimulation, such as having a more extensive social network,
have beneficial long-term effects on cognitive functioning.
General discussion
The overarching aim of this thesis was to investigate if aspects of social
relationships and leisure activity can affect memory functioning and the risk
of incident dementia. To this end, longitudinal data from the Betula project
and VIP were analyzed.
In Study I, higher levels of social leisure activity and total activity in
old age (• 65 years) were associated with reduced risk of dementia over a
follow-up period of 15 years. The findings are consistent with previous
findings that a higher degree of participating in leisure activities late in life
reduces the risk of dementia (for reviews, see Fratiglioni et al., 2004; Stern &
Munn, 2010; Wang et al., 2012). In Study II, social relationships (frequent
visiting/visits of friends and higher levels on a relationship index) were
associated with reduced risk of dementia and AD. These results are in
agreement with some other studies showing a higher level of participation in
social relationships to be associated with lower risk of dementia and AD
(e.g., Bickel & Cooper, 1994; Crooks et al., 2008; Fratiglioni et al., 2000;
Helmer et al., 1999).
60
In both Studies I and II, however, the observed association was no
longer significant after removing from the analyses cases with dementia
shortly after baseline (Study I, up to five years; Study II, up to three years).
Thus, although protective short-term effects cannot be excluded, the
observed associations might be due to reverse causality. Study III, however,
showed that social network size was positively related to cognitive change
(episodic memory and semantic memory) over a follow-up period of 10 years
in a middle-aged sample (40–60 years), although the explained variance was
small. Together, the results presented in this thesis do not provide strong
support for the cognitive reserve hypothesis in relation to dementia.
However, in terms of cognitive functioning there is at least a weak
association, and this is in line with the prediction that social networks might
stimulate aspects of memory and thereby reduce or delay the onset of the
age-related decline.
Results in relation to the cognitive reserve hypothesis
Although, the results from Studies I and II do not provide strong support for
the cognitive reserve hypothesis, Study III showed that social network size
was associated with cognitive change. The beneficial effects are opposite to
the only prior study that we know of (Green et al., 2008) that also targeted
middle-aged (M = 47.3 years at baseline), because they did not find that
social network size was associated with cognitive change (MMSE, delayed
recall). Considering the fact that onset of decline, at least in “fluid” abilities
(e.g., speed of processing, visuospatial ability), starts in late middle-age (e.g.
Rönnlund & Nilsson, 2006a; Rönnlund et al., 2005; Schaie, 1994), the
results of Study III are interesting. If social network size can moderate the
negative age-related influence on declarative memory functions and promote
growth and reduced negative changes in regions vital for these abilities, such
as the neocortex and the hippocampus (Moscovitch et al., 2005), a larger
social network might put an individual on a cognitive trajectory that is
beneficial in old age. Considering that semantic ability is quite robust even in
old age and episodic memory is more age-sensitive (Rönnlund et al., 2005),
the results are promising because effects were found also for episodic
memory. It is also important to note that middle age is often characterized
by a steady decrease in the number of friendships (Wrzus et al., 2013).
When also taking into account the covariates used in studies, it should
be noted that education, a well-established cognitive reserve index that has
been associated with both dementia (for review, see Valenzuela & Sachdev,
2006) and cognitive functioning (e.g., Farmer, Kittner, Rae, Bartko, &
Regier, 1995; Lyketsos, Chen & Anthony, 1999), was not a robust predictor in
any of these three studies. In Study III, years of education were positively
associated with 5-year memory changes (episodic, semantic, and
visuospatial), but not with 10-year changes. It should be noted that Sharp
61
and Gatz (2011) in their review reported a relatively even distribution
between studies that observed (58%) and did not observe (42%) a significant
association between educational attainment and risk for dementia. Physical
exercise, previously related to cognitive functioning (e.g. Etnier et al., 1997)
and a factor that can potentially create a cognitive reserve, was not
associated with cognitive change over 10 years. Thus, considering all
variables used in these three studies, few can be said to support the cognitive
reserve hypothesis.
To put a counterweight to the lack of impact from factors that have
been associated with the cognitive reserve, however, other factors previously
associated with cognitive functioning such as depressive symptoms (e.g.,
Zelinski & Gilewski, 2004) and alcohol use (Anstey et al., 2009) were
unrelated to cognitive change over 10 years in Study III. Similarly, in Studies
I and II, few covariates were associated with dementia, although it should be
noted that the results are mixed regarding, for example, smoking (for review
and meta-analyses, see Cataldo et al., 2010; Peters et al., 2008), alcohol use
(for review and meta-analyses, see Anstey et al., 2009; Peters et al., 2008),
stress (Sundström et al., 2014), and depressive symptoms (e.g., Becker et al.,
2009; Lindsay et al., 2002) in relation to dementia and AD.
An important question is whether beneficial effects of cognitive
stimulation should be expected. Even if many studies have used similar age
groupings as the ones included in this thesis and reported different
outcomes, it might be important to point out that the average baseline age
for the participants was relatively high, at least in Study I (73.70 years) and
Study II (74.20 years). Results from these showed, overall, although not all
of this was not presented in the articles, that age and ApoE-‫ܭ‬4 status were
the strongest predictors of AD and all-cause dementia and that female
gender was related to AD. APOE status has previously been associated with
both all-cause dementia and AD (Farrer et al., 1997; Frisoni et al., 1994;
Stengard et al., 1995; Treves et al., 1996), whereas female gender has been
related to higher risk of AD (Andersen et al., 1999; Gao et al., 1998).
Results of this thesis raise the question of whether or not it is possible
that in old age genetics and other fixed factors become more prominent
predictors or whether or not the cognitive trajectory is more or less set when
reaching old age. In line with the cognitive trajectory being more or less set,
study findings show that the genetic influence on cognitive functioning
increases steadily over time from childhood to old age (Plomin & Petrill,
1997). It should be noted, though, that being widowed (but not single or
divorced) and being non-obese were associated with higher risk in some
models. This is in agreement with some previous studies regarding
widowhood (e.g., Sundström et al., 2014) and studies showing that high BMI
is associated with reduced risk in old age (Fitzpatrick et al; 2009) because
62
low BMI can be a part of the prodromal phase of dementia (Buchman et al.,
2005; Johnson et al., 2006).
Support for an active or passive model?
In this thesis, the cognitive reserve model was investigated from the
perspective of an active model. Our results did not reveal any long-term
effects of enrichment factors undertaken in old age. Associations were found
when investigated earlier in life (midlife), although they were moderate.
Thus, following this pattern, it is reasonable that enrichment factors
undertaken at even younger ages have a larger impact on cognitive
functioning given that brain plasticity is greater in youth or early adulthood.
Furthermore, it is not hard to believe that the formation of brain networks
and cognitive components created earlier in life have larger impacts in old
age.
In the present studies, it was not possible to link activity levels in
young adulthood or middle age to cognitive functioning in old age. Even if
previous studies have suggested that midlife or early life engagement in
leisure activities (e.g. Carlson et al., 2008; Crowe et al., 2003; Friedland et
al., 2001; Fritsch et al., 2005) and social relations (Håkansson et al., 2009)
are associated with decreased risk of dementia, an important question is still
how much and when the cognitive reserve is modifiable over the life course.
Longitudinal data (e.g. Rönnlund et al., 2005) support the idea that several
cognitive functions seem to be relatively stable over the life course (e.g.
episodic and semantic memory) until reaching old age, but there is little
support for significant increment in capacity, at least for fluid abilities, after
the age of 20 (see Park et al., 2002). Based on this, a logical assumption is
that cognitive capacities in general only significantly improve before
reaching adulthood. Thus, is it possible that the cognitive reserve follows the
same pattern? For psychometric intelligence, it has, for example, been found
that the correlation between performance on a manifest intelligence test
taken at age 11 and age 77 is as high as r = .63 (Deary, Whalley, Lemmon,
Crawford, & Starr, 2000). Similarly, other studies have reported high
correlations in cognitive/intellectual performance between different ages, for
example r = .68 between the ages of 14 and 42 (Kangas & Bradway, 1971) and
r = .78 between the ages of 19 and 61 (Owens, 1966). Furthermore, there are
studies suggesting that mental abilities during childhood (e.g. Whalley,
Starr, Athawes, Hunter, Pattie, & Deary, 2000) and young adulthood (e.g.
Snowdon, Kemper, Mortimer, Greiner, Wekstein, & Markesbery, 1996) can
be associated with future cognitive functioning and risk of dementia.
If future cognitive functioning can be predicted by neurological
components established earlier in life, the brain reserve concept (Katzman,
1988; 1993) to some degree seems applicable. Karama et al (2014), for
example, found that cognitive ability in childhood (11 years), accounted for
63
more than two-thirds of the relationship between cognitive ability (70 years)
and cortical thickness (73 years) in old age, an indicator that can be used for
the passive models. Cortical tissue volume is often assumed to be related to
cognitive function. The authors suggested that future studies on cognitive
aging should, if possible, take into account cognitive status in childhood.
However, Pillai et al (2012), for example, aimed to investigate whether
educational level could be related to cortical thickness in old age. The results
showed a pattern opposite to that expected, i.e., that higher education
instead was associated with a thinner cortex. Furthermore, the degree of
atrophy in brain regions related to intellectual variability showed no
difference between educational groups. The authors concluded that early-life
education did not protect against dementia diseases through increased
cortical thickness (brain reserve index) but that educational level was
associated with better performance on cognitive tasks, which is more in favor
of an active model.
Even if it seems reasonable that enrichment factors are most
beneficial for the cognitive reserve when encountered at younger ages, it
should be taken into account that when we reach adulthood most of the
brain has been functionally and structurally specialized for a variety of
environmental demands (Johnson, 2001). Therefore, considering the high
correlations in cognitive/intellectual capacity from young age to old age, how
modifiable the reserve capacity is over the life span should be investigated.
What is required to affect the cognitive reserve?
Cognitive challenges that can serve to produce broader effects on cognitive
functioning would, of course, have important implications for the aging
society. More knowledge is needed concerning what level, amount,
frequency, and type of cognitive challenge is most effective in increasing the
proposed reserve capacity. Similar questions, how cognitive challenges can
exert more widespread influences on cognitive functioning, are today
important questions in cognitive training programs. Herein, the term
“transfer” is often used to describe the phenomenon when effects of training
(or other experiences) are spread to other tasks and/or processes than those
explicitly practiced (Barnett & Ceci, 2002).
So far, most cognitive training programs on humans have reported
relatively domain specific and near transfer effects. This means that when
transfer occurs, these are primarily restricted to highly similar situations
(Detterman, 1993) referred to as “near transfer” (Sandberg, Rönnlund,
Nyberg, & Stigsdotter Neely, 2014). In general, far transfer effects to other
areas than those being practiced have been hard to demonstrate (Kalat,
2013). Most training studies, however, have the lack of long-term intense
multi-modal training. A recent study by Schmiedek, Lövdén, and
Lindenberger (2010) found, by using an intense training program (100
64
hours), that multi-modal training in episodic memory, perceptual speed, and
working memory could generate transfer to untrained tasks. Thus, if similar
results can be confirmed in future studies, cognitive training has the
potential to provide new insights into how cognitive challenges can improve
cognitive functioning and, in theory, how cognitive challenges can potentially
influence cognitive reserve capacity.
More knowledge is also required about how generalizable the effects of
transfer found in laboratory settings are to everyday life and vice versa. This
would have practical implications for the aging society.
Effects might differ depending on outcome
Although it is possible that broader effects of cognitive stimulation may
differ depending on when these occur during the lifespan, it might also be
that effects differ depending on what outcome is measured. As previously
noted, there is support for distinguishing normal aging from pathological
aging. Dementia diseases are chronic and progressive disorders (Kurz &
Lautenschlager, 2010) and are diseases for which there are no cures.
Therefore, even if cognitive impairments can be postponed in pathological
aging, the underlying factors that drive cognitive changes might be stronger
than those causing changes in normal aging, and it is possible that effects
might differ depending on the outcome of normal cognitive aging compared
to pathological aging. Thus, even though beneficial long-term effects were
found among the middle-aged for certain memory abilities in Study III, what
relevance these might have for cognition and risk of dementia later in life
remains an unanswered question. Furthermore, because the amount of
variance in memory ability and change explained by social network size was
still relatively small, caution should be taken not to draw any strong parallels
between the effects found in Study III and risk of dementia in later life.
Cognitive reserve or reverse causation?
It should be noted that there are criticisms to the hypothesis that an active
lifestyle is beneficial for cognitive functioning. Salthouse (2006) argues that
there is little empirical evidence that cognitive aging is moderated by the
amount of mentally stimulating activity. Salthouse argues that research often
draws incorrect conclusions and that it is not at all certain that changes in
activity level can predict cognitive capacity. He claims that it is important to
consider that cognitive activity at any given age can be predicted by current
and former cognitive capacity. According to Salthouse, much of what is
assumed today is based on the desire to believe that cognitive exercise is
beneficial for cognitive functioning.
Even if criticism has been raised towards the idea that cognitive
stimulation might affect cognition, it should be noted that results from
previous studies that have used other statistical methods in elderly
65
populations, have found that social participation (e.g., leisure and social
activities) and social relations can predict future perceptual speed (Lövdén et
al., 2005) and memory decline (Ertel et al., 2008) with less evidence of
reverse causation. Thus, even if firm conclusions cannot not drawn in regard
to directionality of the influence between social relationships and memory
abilities in study III, it is plausible to believe that larger social networks
might be beneficial for cognitive functioning from a long-term perspective.
In Studies I and II, however, the results might indicate a reverse
relationship because effects were only found when near-onset participants
were included in the analyses. The results of these studies are similar to
previous studies that have used relatively short follow-up periods (maximum
5 years) after baseline measurement of both late-life mental and social
activity (e.g., Akbaraly et al., 2009; Fabrigoule et al., 1995; Wilson et al.,
2002a) and social relationships (e.g., Crooks et al., 2008; Helmer et al.,
1999). Hence, there is the risk that previous studies were influenced by
reverse causality. In Studies I and II, the number of participants decreased in
the analyses after excluding those who became demented shortly after
baseline. Even if the probability of a significant association decrease when
reducing the number of participants from the analyses, the hazard ratios in
both in Studies I and II also changed thereby indicating weaker associations.
Only a few studies investigating if leisure activity and social relations
are protective against dementia in old age have controlled for the risk of
reverse causality. These studies have, in contrast to Studies I and II, found
beneficial effects. Wang et al. (2002) used a time period of three years
between measurement of participation in leisure activities and first followup, in which participants were required to still be non-demented, and found
that frequent participation in both mental and social-oriented activities were
associated with decreased risk of dementia in old age (• 75 years). With a
similar study design, Paillard-Borg et al (2009) found that higher factor
scores on mental and social dimensions were associated with a lower risk of
dementia. Regarding social relationships, Amieva et al. (2010) used a
delayed entry of five years when investigating if certain aspects of social
relations were associated with dementia in old age (• 65 years) and found
that qualitative rather than quantitative aspects of social relations might
serve to protect against dementia. Thus, despite the fact that many studies
might have been influenced by reverse causality, there is support for the
notion that the level of cognitive enrichment might predict the risk of
dementia.
Strengths
Strengths of the studies included in this thesis must be highlighted. All of the
studies included in this thesis involved longitudinal data with long follow-up
periods. As can be seen from Studies I and II, results were modified
66
depending on follow-up period. To be able to carry out such analyses, longer
follow-up periods are required. Furthermore, as can be seen in Study III, the
results differed depending on cross-sectional or longitudinal analyses.
Overall, as pointed out earlier, longitudinal data are essential for examining
the relationship between enrichment factors and cognitive functioning.
Furthermore, the studies also used data from a large randomized
population-based sample, and in all studies the analyses were adjusted for a
variety of potentially confounding factors. All these factors are key aspects if
the intention is to make generalizations and to achieve credibility. However,
several limitations must be considered.
Limitations
Although it seems reasonable to make certain interpretations about the
directionality between variables used in the studies, associations between
variables are the only thing that can be determined. To have something of
substance to discuss regarding cause and effect, information about covariation between variables over time is valuable. For example, in Study III,
given the relatively few participants who contributed with longitudinal data
over time regarding social network size, it was not possible to reliably
analyze the co-variation between network size and cognition. Even if the use
of longitudinal data facilitates the ability to examine predictions, we cannot,
as discussed regarding probable reverse causality, truly determine the
direction between variables. It might also be necessary to use a withinsubjects technique to truly investigate whether or not changes in activity
level can influence cognition.
Furthermore, most prominent in Studies I and II, measures of social
relationships and leisure activity were used as the basis for analyses up to 16
years after baseline. Whether one measurement point in time is reliable
when investigating social life and engagement in leisure activity as predictors
of future risk of dementia must be discussed. It has been demonstrated,
however, that lifestyle patterns are relatively stable. Mulder, Ranchor,
Sanderman, Boumaa, and Heuvelac (1998) found that when including
measures of smoking, alcohol consumption, physical activity, and dietary
habits among men, almost 50% of the participants did not change their
patterns in any of the domains over a period of four years. Among those who
changed, 40% only changed in one lifestyle behavior, and only a small
portion (11%) changed in two or more behaviors. Furthermore, we do not
know if the participants continued to be active or not after baseline or had
other changes in their personal lives. Unfortunately, it was not possible to
investigate longitudinal changes in relation to dementia due the small
number of participants who had repeated measures on leisure activity and
social relationships.
67
In all three studies, composite measures were used as indexes. The
main reason for this was the assumption that overall stimulation (i.e. the
more the better), at least in theory, should generate more cognitive
stimulation. We further labeled the composites as, for example, "mental
activity", "social relationships", and "social network size", a method that is
not straightforward. First, it is unlikely that these composites are mutually
exclusive and only represent what they are supposed to measure. For
example, items included, such as attending courses/workshops, is perhaps
not only a social activity but is also a mental activity. In the same vein,
indexes might represent dimensions not in the scope of this thesis. For
example, the index of social relations used in Study II, including questions
about satisfaction with contact frequency and having a close friend, might be
very much related to other aspects than cognitive stimulation (this was the
motivation to investigate each item separately in Study II). Although the
desire was to conceptualize every composite used, there are always problems
defining and operationalizing such concepts. In addition, the mix of
questions, and the use of dichotomized and categorical data in composites,
gives a rather coarse measure from a psychometric point of view.
It might be important to report the internal consistency of the
composite measures that are used, as it was in Study I. The values for the
indexes were low, especially for “mental activity.” It should be noted,
however, that the choice of one leisure activity might decrease the time
available to engage in other activities, and it is also not certain that
individuals who are active in a so-called “social activity” automatically
engage in other “social activities”. Cronbach’s alpha might therefore not be a
fair index to evaluate reliability. It was decided to allow participants to
subjectively categorize the activities as predominantly mental, social, or
physical activities. However, the low Cronbach’s Į, especially for the mental
index, is of concern because it limits the ability to draw any firm conclusions
about the association between the mental activity and dementia.
It is also possible that the activities and relations included in this
thesis are not challenging enough cognitively to have any influence on the
outcome variables used. Several of the factors, for example, reading
magazines, watching movies and attending concerts, or just having a close
friend say little about cognitive challenge. Including measures of cognitive
demand and/or indicators of novelty seeking (interest in new activities or
social relationships) might have altered the results. Unfortunately, we lacked
information about how cognitively challenging the participants believed the
factors were.
The issue of whether or not the effects of activities or social
relationships are dependent on the type of dementia is a relevant question.
Given that a sufficient number of cases, for instance, of vascular dementias
were not available, in particular in the analyses where cases with short
68
survival were excluded, such analyses were not meaningful in the present
study.
Finally, another important aspect that should be considered is the
extent to which the Betula sample is representative of the target population.
Nilsson et al. (1997) showed that the Betula sample has a good population
validity with regard to several factors, including gender, marital status,
employment, education, income, and number of persons in the household.
Although the sample might be representative in terms of several
demographic factors, it is still possible that those who chose to participate
were more likely to have better health status compared with nonparticipants (see Drivsholm et al., 2006) resulting in a selection bias towards
participants with a more active and socially integrated lifestyle that might
have blurred potential associations.
Implications and future directions
Studies I and II have important implications in regard to methodology and
show that future studies would benefit from using longer follow-ups and to
adjust for reverse causality.
Regarding Study III, even if the variance explained by the social
network was small, the results must be put into perspective of how robust
our memory functions are over most of middle age, particularly in terms of
episodic and semantic memory. Although strong conclusions cannot be
drawn, the knowledge that social network size can have positive effects on
cognitive performance has implications for the future. In an aging
population, it might be important to know if interaction in larger social
networks in midlife might have beneficial effects when entering old age.
Some important topics for future research have already been
mentioned, such as the importance of examining co-variation over time in
order to investigate the causal relationship between enrichment factors and
cognition. It is also important to investigate how modifiable cognitive
reserve is across the life span and what cognitive challenges are needed to
affect the reserve. In addition to the shortcomings of the scales used in the
present studies, there is an urgent need to find new methods to capture
patterns of everyday life. Although this is not unproblematic, considering
community development and how rapid technological and informational
expansion are constantly opening up new alternate forms of leisure activities
and social relationships (i.e. social media), an adjustment for this in the
design of new instruments, to the extent that it is possible, is required.
As can be seen in reviews regarding, for instance, leisure activity (e.g.,
Fratiglioni et al., 2004; Stern & Munn, 2010; Wang et al., 2012), there are a
variety of instruments used to measure individual patterns. Although more
or less formal activity scales are sometimes used, such as the Adelaide
Activities Profile (Clark & Bond, 1995), or the RIASEC activity list (Parslow
69
et al., 2006), these are, as are most other scales, based on a number of
predetermined activities, and this might limit the possibility of capturing the
broad spectrum of leisure activities available in today’s society. One possible
way to solve this challenge is similar to that of Paillard-Borg et al (2009) who
used an open-ended question method of measuring activity type and
frequency of engagement. This is not only a relatively simple method, but it
is also an approach that could be used in future studies to capture individual
patterns. Although this can make it more problematic to manage the data,
the open-ended question approach might be the best way to capture the wide
range of activities and social networks that people engage in today. Diary
studies might also be a reliable way to capture everyday life in regard to
leisure activities. In addition, future studies will have to provide measures
that capture all possible forms of activities and social relationships that the
digitally networked world allows.
Most previous research on cognitive aging has focused on deficits that
often come with aging and on differences between normal and pathological
aging. Little attention has been paid to those individuals who actually show
little or no cognitive decline. Data from the Betula Project (Nilsson et al.,
1997, 2004) show that about 10% of the participants over 70 years are still
relatively stable in performance and do not indicate any signs of decline.
Nyberg et al. (2012) discuss a complementary concept to the cognitive
reserve, referred to as brain maintenance, and they highlight that some
elderly have a relative lack of brain pathology and have well-preserved
memory function. Furthermore, when comparing younger adults and highperforming older adults, the activation pattern between them is often
similar. More studies should be directed toward those who maintain
neurochemical, structural, and functional brain integrity in old age.
As evidenced in Studies I and II, the integration of several other
disciplines in the research of memory, including psychiatry, genetics, and
epidemiology, not only makes it possible to obtain a more complete picture
of the cognitive aging process, it also provides greater understanding of
individual differences. The development of brain imaging techniques such as
position emission tomography and functional magnetic resonance imaging
provide opportunities to longitudinally investigate how changes in structure
and brain activity affect cognitive functioning. In addition, such methods
also provide possibilities to investigate individual changes, what
characterizes those changes that preserve cognitive functioning in old age,
and how such differences are influenced by environmental factors.
Sophisticated neuroimaging methods might also be useful as diagnostic tools
to identify early signs of pathological disease progression and might be
helpful for developing pharmacological and/or cognitive treatments in the
future.
70
Future interventions, including intense multi-modal training
programs together with brain imaging techniques, might allow for a deeper
understanding of parameters associated with changes in brain structure and
function. Refined methods might also give the opportunity to investigate the
cognitive reserve more closely because little is currently known about the
architectural neural basis of the reserve (Esiri & Chance, 2012). Thus,
investigating how individuals with similar brain pathologies process
cognitive information differently depending on different cognitive reserve
indicators might give new insights into the reserve concept. Thus, future
interventions and cognitive training programs should be integrated with
imaging techniques in order to investigate whether or not changes in lifestyle
activities can cause changes in structure and function (and thus in
performance) and thereby have positive effects on everyday functioning.
Finally, as shown in Studies I and II, genetics is an important aspect of
cognitive aging. Identification of additional genetic factors, and further
knowledge of how environmental factors might influence our individual
genetic characteristics and cognitive progress, should enhance the
understanding of how to postpone cognitive decline and dementia.
Conclusions
Results of the studies included in this thesis suggest that cognitive
stimulation in middle age (40-60 years), such as having a large social
network, has beneficial effects on cognitive functioning. Enrichment factors
measured in old age (• 65 years), however, such as engagement in leisure
activities or having a number of social relations, did not alter the risk of allcause dementia or AD when near-onset dementias were removed from the
analyses. Such results highlight the importance of having a long follow-up
period and adjusting for reverse causality when investigating the impact of
an active lifestyle among the elderly. Even if the results showed that age,
gender, and genes are of significance for an increased risk of dementia
and/or AD, finding evidence for factors that can be influenced by changes in
behavior, such as other aspects related to leisure activity and social
relationships, are of future interest. More studies are needed that 1) include
measures of cognitive stimulation over the entire life-span, 2) that use
refined instruments that capture a broader spectrum of everyday life, and 3)
that integrate other research disciplines such as neuroimaging. Future
studies should also identify factors that characterize brain maintenance and
that examine potential interactions between genes and the environment in
order to investigate the influence of cognitive stimulation as a way to
postpone dementia diseases.
71
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