The association between changes in physical activity
and mental health among older adults in Ireland
Aideen Sheehan
September 2016
This thesis is submitted to the School of Social Work and Social Policy, Trinity
College Dublin in partial fulfilment of the requirements for the degree of
Masters in Applied Social Research
under the supervision of Professor Richard Layte
Declaration
I declare that this thesis is entirely my own work. It has not been submitted to this university
or any other institution for degree or publication.
I authorise the University of Dublin, Trinity College to lend this thesis to other institutes or
individuals for the purpose of scholarly research. I further agree that this thesis may be
copied, in whole or in part, at the request of other institutes or individuals for the purpose
of scholarly research.
______________________________
Aideen Sheehan
Date
ii
Acknowledgements
I would like to thank Professor Richard Layte for his expert guidance as my supervisor, my
colleagues at TILDA for their extremely helpful advice and all the team on the Applied Social
Research course for bringing us through a steep learning curve in the past year. A special
thanks to Liam and Laila for their good humour and patience while I completed it, and to
Nana for all the babysitting that made it possible.
iii
Abstract
Background: Depression affects one in 10 older adults in Ireland and the large majority of
these are untreated and undiagnosed. Physical activity is associated with better mental
health and well-being, though the scale of this relationship remains contentious and the
impact of changes in activity patterns in later life has been under-explored.
Methods: Secondary analysis of TILDA data focussing on a subset of 6871 older Irish adults
at two different time-points was conducted to explore the relationship between changes in
physical activity and changes in depressive symptoms. A hierarchical regression model was
used to examine the association while controlling for other social, health and economic
changes that may also have influenced mental health. A logistic regression model was also
developed to examine the likelihood of developing depression when controlling for other
changes.
Results: Becoming or remaining active were found to be significantly associated with a
reduction in depressive symptoms and a reduced likelihood of developing depression,
although the overall effect was small. Becoming inactive was not found to be a significant
factor in mental health outcomes.
Conclusions: The findings support the hypothesis that increasing activity or remaining active
are both associated with better mental health. Public health campaigns to highlight the
benefits of increased activity for mental wellbeing in later life may be warranted. Further
research using objective measurements of physical activity would be useful.
KEYWORDS: Physical activity; exercise; older adults; mental health; depression; Ireland.
iv
Table of Contents
List of Tables
viii
List of Figures
viii
Introduction
1
Chapter 1: Literature Review
3
1.1 Introduction
3
1.2 Prevalence of depression and mental illness
3
1.3 Physical activity worldwide and targets
4
1.4Link between physical activity and mental health
1.4.1 Meta-analyses and debate
5
5
1.4.2 Longitudinal studies
1.5 Pathways for Link Between Physical Activity and Mental Health
1.6 Models of Change and Barriers to Taking up Physical Activity
1.7. The Irish Context
9
11
12
13
1.7.1 Physical activity levels in Ireland
13
1.7.2 Mental health in Ireland
14
1.7.3 Link between physical activity and depression among older people 15
1.7.4 Economic cost of poor mental health in Ireland
15
1.8 Research Questions
16
Chapter 2: Methodology
17
2.1 Introduction
17
2.2 TILDA
17
2.2.1 Design and Methodology
18
2.2.2 Ethics and Access
21
2.3 Weights
22
2.4 Sample profile
23
2.5 Variables for analysis
26
2.5.1 Dependent variables
v
26
2.5.2 Independent variables
27
2.5.3 Control variables
29
2.6 Data analysis
31
2.6.1 Stage 1. Wave-specific analysis
2.6.2 Stage 2. Changes in activity and mental health
31
32
2.6.3 Stage 3. Controlling for other factors
32
Chapter 3: Findings
33
3.1 Introduction
33
3.2 Section 1. Wave-specific analysis
34
3.2.1 Demographic differences of physical activity groups
34
3.2.2 Mental health of different activity groups
38
3.2.3 Summary of Section 1 findings
41
3.3. Section 2. Changes in physical activity and mental health between waves
42
3.3.1 Changes in physical activity between waves
42
3.3.2 Mental health changes between waves
46
3.3.3. Summary of Section 2 findings
50
3.4 Section 3. Changes in mental health controlling for other factors
51
3.4.1 Changes in social, economic and health circumstances
53
3.4.2 Model of change in depressive symptoms
53
3.4.3 Regression results
56
3.4.4 Likelihood of developing clinical depression
56
3.4.5 Summary of Section 3 Findings
58
3.5 Recap
58
Chapter 4: Discussion
59
vi
4.1 Introduction
59
4.2 Characteristics associated with activity change
59
4.3 Changes in physical activity and mental health
60
4.4 Activity changes
61
4.4.1 Timing as factor
62
4.5 Social relevance
63
4.6 Evaluation of study
63
4.6.1 Limitations
4.7 Further research
64
64
4.8 Conclusion
Bibliography
65
Appendices
Appendix 1. IPAQ short form
71
Appendix 2. CES-D depression questionnaire
72
Appendix 3. Residuals plot of final regression model of depressive symptoms
75
Appendix 4. Cooks Distances plot of regression model of depressive symptoms
76
vii
List of Tables
Table 1.1 Meta-analyses of impact of physical activity on depression
Table 2.1 Reasons for non-participation at Wave 2
Table 2.2 Profile of participants at each wave and those who dropped out
Table 2.3 Physical activity classifications
Table 3.1 Characteristics of different activity groups at Wave 1
Table 3.2 Characteristics of different activity groups at Wave 2
Table 3.3 Cross-tabulation of physical activity and depressive categories at Wave 1
3.4 Cross-tabulation of physical activity and self-reported emotional/mental health
3.5 Cross-tabulation of physical activity and depressive categories at Wave 2
3.6 Cross-tabulation of physical activity and self-reported emotional/mental health Wave 2
3.7 Cross-tabulation of activity level at Wave 1 and Wave 2.
3.8 Characteristics of activity change groups
3.9 Cross-tabulation of physical activity changes and new cases of clinical depression
3.10 Cross-tabulation of changes in physical activity and self-rated emotional mental health
3.11 Changes in physical activity and social, economic and health changes since Wave 1.
3.12 Hierarchical multiple regression model results
3.13 Logistic regression model predicting odds of developing clinical depressive symptoms
List of Figures
Figure 2.1 Study sample
Figure 3.1 Season of Wave 1 interview
Figure 3.2 Season of Wave 2 interview
Figure 3.3 Change in depressive symptoms by depressive category at Wave 1
Figure 3.4 Change in depressive symptoms by depressive category at Wave 2
Figure 3.5 Change in depressive symptoms by activity group
viii
Introduction
Depression is one of the leading causes of disability worldwide affecting 350m people
(WHO, 2016) and costing the world economy over $2.5trillion in lost output each year
(Chisholm et al, 2016). However stigma and a lack of resources mean most sufferers do not
receive treatment and there are 800,000 deaths from suicide each year (WHO, 2016).
Amongst older people depression is associated with disability, increased mortality and
poorer health and a higher suicide rate than for younger adults (Rodda, 2011). In Ireland
10% of the population aged 50 and over has been found to have clinically relevant
symptoms of depression but only one in five of these has received a doctor’s diagnosis
suggesting it is widely untreated (O’Regan, Cronin and Kenny, 2011). Poor mental health is
estimated to cost Ireland €3bn per annum (O’Shea and Kennelly, 2008).
Physical inactivity meanwhile is ranked as the fourth biggest risk factor for global mortality
with proven links to bowel cancer, diabetes and heart disease (WHO, 2010). The association
between physical activity (PA) and mental health has been widely studied, but though the
evidence indicates an association between activity levels and depression the precise scale of
this link remains contentious. Nonetheless the World Health Organization has concluded
that PA can reduce symptoms of depression and possibly stress and anxiety and these
findings have fed into its recommendations that all adults and older people do at least 150
minutes a week of moderate intensity activity (WHO, 2010).
However worldwide levels of PA are very low and just over a third of Irish adults meets
WHO and Irish government recommendations (DoH, 2016). Research has shown that highly
active older adults in Ireland are less than half as likely to suffer clinical symptoms of
depression as their inactive peers and even walking over 150 minutes a week is associated
with better mental health and wellbeing (Donoghue et al, 2016). However all Irish studies so
far have been cross-sectional making it impossible to assess whether older people who
enjoy better mental health are more likely to engage in physical activity, or whether being
more physically active leads to better mental health. Internationally a number of studies
have been carried out to explore the link between changes in PA and changes in mental
1
health (Yoshida et al, 2015; Hamer, Lavoie and Bacon, 2014), but no Irish research has been
published into this association.
This study will use quantitative secondary analysis of two waves of a large longitudinal
sample of older Irish adults to examine changes in physical activity in later life and changes
in mental health. It will explore whether those who became more physically active
experienced improvements in mental health – and specifically a reduction in depressive
symptoms - compared to those who reduced their activity or remained inactive. It will also
examine whether any changes that are seen persist when controlling for other social, health
and economic factors that may have influenced outcomes.
2
Chapter 1: Literature Review
1.1 Introduction
Depression is one of the leading causes of disability worldwide (Mnookin, 2016) and as the
number of people affected worldwide rises (Hidaka, 2005; WHO, 2016) increased attention
has been paid to physical activity as a treatment and lifestyle option that can mitigate or
reduce the enormous burden it imposes on individuals and society. While a positive
association between physical activity and good mental health – and conversely between
depression and inactivity – has been found, the precise extent of this connection remains
contentious. In this review I will outline the scale of the depression epidemic, the evidence
for a link between PA and depression, the suggested pathways for how this link operates
and Irish research into depression and physical activity in the older population for whom
good mental health has been found to outrank physical health and economic resources in
predicting quality of life (Layte, Sexton and Savva, 2013).
1.2 Prevalence of depression and mental illness
Worldwide some 350 million people suffer from depression and it will be the second leading
cause of disability by 2020 according to the World Health Organisation, while 800,000 lives
are lost to suicide each year (WHO, 2016). Around 10% of the world’s population is
estimated to suffer from a mental health disorder and it accounts for 31% of the non-fatal
disease burden globally (Mnookin, 2016). The 12-month prevalence rate for depression
worldwide is 5%, ranging from 2.2% in Japan to 10.4% in Brazil (Kessler and Bromet, 2013).
The Global Burden of Disease study puts the point prevalence of depression at 3.2% for men
and 5.5% for women, while for anxiety disorders it is put at 7.3% (Chisholm et al. 2016).
Lifetime prevalence meanwhile for mood disorders including major depression and bipolar
disorder is estimated at over 20% (Kessler, Berglund, Demler, Jin, Merikangas, Walters,
2005). Evidence suggests the rate of major depression is rising especially in more developed
countries (Hidaka, 2005), which he attributes to competitive modern lifestyles: “Modern
3
populations are increasingly overfed, malnourished, sedentary, sunlight-deficient, sleepdeprived, and socially-isolated,” (Hidaka, 2005:205).
However stigma and a lack of resources mean that only between 7% and 28% of those
suffering from depression receive treatment – with those in poorer countries least likely to
get it - despite the fact it costs the world economy an estimated US$2.5-8.5 trillion in lost
output each year (Chisholm et al, 2016). Amongst older people depression is also
associated with disability, increased mortality, poorer health and dementia as well as a
higher suicide rate than for younger adults (Rodda, 2011). This enormous human and
economic burden has led to improvements to mental health treatment being targeted in the
United Nations 2015-30 Sustainable Development Goals (UN, 2015) and to huge scientific
attention being paid to cost-effective methods of lowering the burden.
1.3 Physical activity worldwide and targets
Inactivity has a massive impact on physical health with the World Health Organisation
ranking it as the fourth biggest risk factor for mortality with responsibility for 6pc of deaths
globally each year, including many from bowel cancer, breast cancer, diabetes and heart
disease (WHO, 2010). In a review of the evidence WHO also found an association between
physical activity (PA) and mental health: “Physical activity can reduce symptoms of
depression and, possibly, stress and anxiety” (WHO, 2006: 7). They noted it may also confer
other psychological benefits such as positive self-image and self-esteem. On the basis of the
established health benefits including the reduced risk of depression it has recommended
that all adults should do at least 150 minutes per week of moderate-intensity aerobic
physical activity or 75 minutes of vigorous activity, with added health benefits by doubling
these weekly targets. The same recommendations apply to older adults aged 65 and over,
with the added proviso that those with poor mobility should do activities that enhance
balance and prevent falls on 3 or more days per week, and that muscle-strengthening
activities for major muscle groups should be carried out on at least 2 days per week (WHO,
2010).
Levels of physical activity
However despite the proven benefits, levels of physical activity are very low worldwide with
just 31.1% meeting the recommended target, ranging from 17% in Southeast Asia to 43% in
4
the Americas and the eastern Mediterranean region, while inactivity rises with age, and is
higher in women and high-income countries (Hallal et al, 2012). Levels of PA are similarly
low in Europe with two thirds of people failing to meet the guidelines (WHO, 2006).
1.4 Link between physical activity and mental health
The link between PA/exercise and mental health, particularly depression, has received a
huge amount of attention and been the focus of hundreds of randomized control trials and
other studies. However despite this attention and numerous attempts to synthesize the
results, the efficacy of PA in treating/preventing depression remains highly contentious.
Although most studies point to PA having a positive impact in reducing depressive
symptoms, conclusions about the effect size vary widely. This section summarises some of
the main findings garnered from more recent studies and meta-analyses and the
methodological arguments about why such differences have been found.
The literature is broadly split into studies which examine the impact of physical activity or
exercise on populations suffering from diagnosed mental health disorders such as clinical
depression and those looking at the association between activity levels and mental health
indices within the general population. Most also focus on depression and/or anxiety which
as discussed above are widely prevalent disorders, but also ones for which symptoms exist
on a continuum in the general population and for which well-validated and widely-used
scales of measurement have been developed. Because of the proliferation of studies on
different population groups and widely varying methodologies, the focus of this review of
international literature was on meta-analyses. Randomised control trials of PA interventions
were the dominant study type found, and were the focus of several meta-analyses on the
subject. However there is also a substantial body of research using high-quality survey and
longitudinal data to explore the association between PA and mental health in the general
population and various sub-groups which will also be reviewed.
1.4.1 Meta-analyses
Table 1.1 below summarises the findings from more recent meta-analyses of the link
between physical activity and depression.
5
Table 1.1 Meta-analyses of the impact of exercise/physical activity on depression
Author
(year)
Cooney et al.
(2013)
Publication
Rebar et al
(2015)
Health
Psychology
Review
Cochrane
Database of
Systematic
Reviews
Rethorst et al Sports Medicine
(2009)
Effect
Results
Small to
moderate
Meta-metaanalysis of
RCTs on nonclinical adult
population
Meta-analysis
of RCTs.
Clinically
depressed and
non-clinically
depressed
Meta-analysis
of RCTs on
clinically
depressed
adults
Meta-analysis
of PA
interventions
on nondepressed
adults
Moderate
SMD = -0.62,
[95%CI: -0.81,
0.42]
High quality
trials only SMD
= -0.18, [95% CI:
0.47, 0.11]
SMD = -0.50;
[95%CI: -0.93,0.06]
Moderate to
large, but
small and not
significant at
follow-up.
Moderate
Schuch et al
(2016)
Journal of
Psychiatric
Research
Conn
(2010)
Annals of
Behavioural
Medicine
Kvam et al
(2016)
Journal of
Affective
Disorders
Meta-analysis
of RCTs on
depressed
adults
The Journal of
Gerontopsycholog
y and Geriatric
Psychiatry
Meta-analysis
of PA
interventions
on depressed
older adults
aged 60+
Heinzel
(2015)
6
Study type
and focus
Meta-analysis
of RCTs on
clinically
depressed
patients
Moderate
ES =-0.80
[95%CI: -0.92,0.67]
Large
SMD =1.11
[95% CI:
0.79,1.44]
Adjusted for
publication bias
SMD = 0.372
among
supervised PA
studies and
0.522 among 22
unsupervised
PA studies
g = 0.68.
Small to
moderate
Follow-up g =
-0.22
SMD = -0.68.
When only trials
with low risk of
bias included
SMD = -0.63.
A recent ‘meta-meta-analysis’ of the impact of PA on depression and anxiety in the nonclinical adult population found that it reduced depressive symptoms by a medium effect and
anxiety by a small but significant effect (Rebar, Stanton, Geard, Short, Duncan and
Vandelanotte, 2015). This study aggregated 6 meta-analyses based on a total of 398 studies
over 50 years involving over 14,000 participants. It found that PA reduced depression by a
medium effect [standardized mean difference (SMD) = -0.50; 95%CI: -0.93—0.06] and
anxiety by a small effect [SMD = -0.38; 95%CI -0.66 to -0.11].
A Cochrane systematic review of the effect of exercise on depression from 39 randomized
control trials involving 1356 adult participants with depression meanwhile found that
exercise achieved a greater reduction in depressive symptoms than no treatment, placebo
or interventions such as meditation (Cooney, Dwan, Greig, Lawlor, Rimer, Waugh, McMurdo
& Mead, 2013). However the benefit was small when only high-quality studies were
analysed. It found that exercise was moderately more effective than a control in reducing
the symptoms of depression and was not significantly different from using pharmacological
or psychological therapies (Cooney et al 2013). It also found that resistance exercise had a
stronger impact than aerobic exercise and concluded: “Exercise is moderately more
effective than a control intervention for reducing symptoms of depression, but analysis of
medhodologically robust trials only shows a smaller effect in favour of exercise” (Cochrane
et al 2013: 3).
However despite the widespread acceptance of Cochrane reviews as a gold standard of
evidence synthesis, there have also been criticisms from within the exercise science
community of that particular review for under-estimating the effect of exercise on
depression, with a recent meta-analysis suggesting that publication bias had led it to
underestimate the effect of exercise on depression (Schuch, Vancampfort, Richards,
Rosenbaum, Ward and Stubbs, 2016). The latter synthesized results from the same RCTs in
the Cochrane review, along with newer studies published between 2013 and 2015 and
concluded that exercise had a large and significant effect on depression (Schuch et al, 2016).
It suggested that the difference between the two reviews was down to publication bias, the
inclusion criteria, the fact that the latter study measured change in depressive symptoms
7
rather than final depression score and its own inclusion of three more recent high-quality
trials.
Another study also accused the Cochrane review authors of “questionable methodological
choices” most notably by including studies where there was no proper control group, but
where the effects of exercise plus medication or psychotherapy were compared to those of
using medication or psychotherapy alone (Ekkekais, 2015:21). It argued there was too much
heterogeneity in the studies chosen to allow for meaningful synthesis and noted that
successive updates of the review between 2001 and 2013 had led to shrinkage of the effect
of exercise by 44%. Ekkekakis (2015) reanalyzed the same dataset and concluded the effect
of exercise was large and that even when only high-quality studies were used this effect size
was significant.
Another meta-analysis attempted to factor out the impact of including treatments such as
meditation or relaxation as a placebo control because of their recognized antidepressant
effect (Josefsson, Lindwall and Archer, 2014). It analysed 13 studies comparing exercise to
no treatment, placebo or usual care among clinically depressed adults and found a
significant large effect. This effect was even larger where the control group received no
treatment, though the effect was moderate when only high-quality studies were included.
Reviews involving both clinical and non-clinical studies
A meta-analysis of 58 randomized trials involving 2982 participants found those receiving
the exercise treatment had significantly lower depressive symptoms at the end (SMD= 0.80) than those receiving the controls (Rethorst et al, 2009). The effect was large and
significant in the clinically depressed population and the change was also significantly larger
than that seen in the general population. The authors concluded this provided “level 1,
Grade A evidence for the effects of exercise upon depression”. (Rethorst et al, 2009: 492).
Meta-analyses in the older population
A systematic review and meta-analysis of 18 exercise trials testing the impact of physical
activity on depression on a total of 1,063 adults aged 60 and over found a moderate effect
8
size, with similar results when trials with a higher risk of bias were excluded, and this effect
held up for all types of exercise. (Heinzel, Lawrence, Kallies , Rapp and Heissel, 2015).
1.4.2 Longitudinal Studies
As well as intervention and randomised control studies there have been numerous
prospective studies using longitudinal data to explore the link between PA and depression
but there are fewer syntheses of these than of RCTs. However a systematic review of 30
mainly high-quality longitudinal studies over at least two time-periods found that 25 of
these showed baseline PA was inversely associated with the risk of subsequent depression
(Mammen and Faulkner, 2013). Eleven of these studies also looked at changes in PA and
depression over time with 9 of them finding a significant relationship. One found that
people who reduced PA were 10 times more likely to suffer from depression while others
found those who increased or maintained PA levels were less likely to become depressed. It
concluded that individuals who are already active should maintain this, while sedentary
individuals should take up PA. “There is promising evidence that any level of PA, including
low levels, can prevent future depression,”(Mammen et al, 2013: 656). Limitations noted
however included different cutoff points for defining depression.
This association did not hold up in every case as a longitudinal study of the relationship
between PA and depression among Norwegian adolescents over 10 years found that though
they were inversely related at baseline level, high levels of PA at baseline did not protect
against later depressed mood, while high depressive symptoms at baseline did not act as a
barrier for later depressed mood. The authors speculated the generally high level of PA
among Norwegian youth might have influenced the results (Birkeland, Torsheim and Wold
2009).
Longitudinal studies of older adults
Turning to longitudinal studies focused on older age-groups, many did find evidence of a link
between PA and depression. A cross-lagged study of the link between PA and depression in
17,593 older adults from 11 European countries across two years found that a higher
baseline level of PA was associated with fewer depressive symptoms at follow-up. However
9
the association between depressive symptoms at baseline and PA at follow-up was not
significant (Lindwall, Larsman and Hagget, 2011).
A study of 6,653 Australian women without depression or anxiety aged 73-78 over three
years found that symptoms of depression and anxiety were inversely related to levels of PA,
with the results supporting a dose-response association which held up across five levels of
activity (Heesch, Burton and Brown, 2011). A US study of 1,947 adults aged 50-94 found that
higher PA was protective against both prevalent and incident depression over 5 years even
when adjusted for numerous confounders including age, sex, financial pressure, chronic
illness, disability, alcohol consumption and social relations (Strawbridge, Deleger, Roberts
and Kaplan, 2002).
A 3-year prospective study in Japan assessed 680 adults aged 65 and over categorized into
four different levels of physical activity based on whether they remained sedentary, became
inactive, became active or maintained physical activity. It found that the group who
remained physically active were less likely to experience depressive symptoms after three
years (OR 0.5, 95%CI, 0.30-0.83) whereas initiating or ending PA did not have a significant
effect (Yoshida et al, 2015).
A UK study using ELSA data to investigate the impact of taking up physical activity in later
life found an association between PA and healthy ageing – including a measure of
depression - with a dose-response relationship (Hamer, Lavoie and Bacon, 2013). Those
who remained active were most likely to remain healthy throughout 8 years of follow-up,
while those who became active were also significantly more likely to be healthy and free of
major depressive symptoms than those who remained inactive.
A study of 1524 Israelis aged 50 or over meanwhile found that though changes in physical
activity and body weight were associated with depressive symptoms, when measures of
health were added, the correlation between commenced activity and depression
disappeared while the correlation between continued activity and depression was reduced
(Khalaila and Litwin, 2014).
10
1.5 Pathways for link between physical activity and mental health
While there is still dispute about the size of the effect of exercise on mental health, research
does agree it has some impact. The exact reasons for this remain unclear, but the main
factors posited are biological, psychological and psychosocial.
Biological
A vast number of biological causes for the impact of PA on mental health have been posited
but there is little consensus about which is the dominant one. In a review of these Lopresti
(2013) looked at:
1) the anti-inflammatory effect of longterm exercise;
ii) the effect of exercise on neurotransmitters such as serotonin
iii) its impact on the hypothalamic-pituitary-adrenal (HPA) axis through altered
levels of cortisone and other brain hormones such as ACTH;
iv) its impact on brain plasticity and the brain growth protein BDNF;
v) its stimulating effect on mitochondria generation.
However that study notes many conflicting findings from the evidence despite promising
paths for research and concludes that the array of lifestyle factors as well as genetic,
psychological and other factors influencing depression mean interventions that targets only
one cause or biological mechanism could make recovery more difficult (Lopresti et al, 2013).
Two recent studies examining the association between inflammatory markers and
depressive symptoms in relation to PA levels found little evidence of a significant
relationship between them (Kop 2009, Hamer et al, 2008). Improved sleep through higher
PA is another potential physical factor in improving mental health with the evidence
suggesting a small to moderate effect (Biddle and Motrie, 2008).
Psychological
Various psychological factors have also been mooted to explain the link between PA and
reduced depression. Among these is the theory that taking up an exercise programme
boosts self-efficacy as found in a study of chronically depressed women by Craft (2005)
11
which found this effect was noticeable after just three weeks. Summarising the evidence for
a link between exercise and self-esteem, Biddle and Mutrie (2008) found a small but
significant positive effect for adults though with some contradictory findings. Regarding the
impact of exercise on affect or positive mood, beneficial effects were found, including for
older people, but this remains quite a difficult construct to define and measure (Biddle &
Mutrie 2008).
Another interesting hypothesis discussed by Stathopoulou et al (2006) is that as affective
disorders such as depression and anxiety are associated with hyperactivity in certain
prefrontal areas of the brain, the fact that cognitive function in these areas was found to
become impaired during moderate exercise (Dietrich & Sparling, 2004) could explain the
anti-depressant and anxiolytic effects of such exertion as a distraction from worries and
rumination.
The simple fact of getting people up and out of the house or encouraging social engagement
in a group have also been posited as potential psychosocial explanations for the positive
impact of exercise on depression (Biddle & Mutrie, 2008) .
1.6 Models of change and barriers to taking up physical activity
While the benefits of PA to mental and physical health have been widely promoted, the low
levels of activity in the population have been a cause of concern, with various theoretical
models to try and explain how people initiate change, but less on why they stop. The Health
Belief Model, the Stages of Change Theory and the Theory of Planned Behaviour all assume
a rational appraisal of the benefits of changed behavior but although different behavioural
management approaches have been used to assist individuals with this, the evidence
indicates that many techniques are only effective short term (Khatta, 2008).
The fact many people seem to guard inactivity regardless of the widely heralded benefits of
activity might also be an evolutionary mechanism designed to reward the safety of staying
put, with some evidence suggesting activity only feels pleasurable up to a certain point of
exertion, after which it becomes less rewarding (Ekkekakis et al, 2005). A review of 33
articles on the subject of exercise and affective response between 1999 and 2009 found
12
mainly pleasant responses to low-intensity exercise, high individual variability at the lactate
threshold, and negative responses for higher-intensity exercise (Ekkekakis, 2011).
1.7 Irish Context
This section examines research into Irish physical activity levels, mental health in Ireland,
the association between physical activity and mental health and the economic cost of poor
mental health in Ireland.
1. 7. 1 Physical activity levels in Ireland
Because of the important health and wellbeing benefits, the Department of Health in
Ireland echoes WHO recommendations that adults and older people aged 65+ should do at
least 30 minutes a day of moderate intensity activity on five days or 150 minutes per week,
with a particular focus for older people on aerobic activity, muscle-strengthening and
balance (DoH, 2009).
However just 32% of the adult Irish population is highly active and meeting the
recommended threshold for healthy levels of physical activity according to a survey of 7,539
Irish adults aged 15 and over (DoH, 2016). Men are significantly more likely (40%) than
women (24%) to be highly active. The levels of activity also decline markedly with age, as
just 15% of adults aged 65 and over were found to be sufficiently active (DoH, 2016)
An earlier study using different data indicated a higher baseline level of activity but also
found a steep decline with age. It found that 37% of those aged 60-64 met the
recommended guidelines (Murtagh, 2014) but this declined to 32% of those aged 65-69, and
continued to fall steeply, with just 18% of those aged 75 or over meeting the recommended
target. This study also found that levels of activity were much lower among older adults in
Northern Ireland where those aged 75 or over were half as likely to meet the target as their
counterparts in the Republic (Murtagh, Murphy, Murphy, Woods & Lane, 2014).
A study using The Irish Longitudinal Study on Ageing (TILDA) data found that physical activity
levels among adults aged 50 and over remained fairly stable overall over two years with 34%
reporting low PA at wave 2 compared to 31.7% at Wave 1 (Finucane, Feeney, Nolan and
13
O’Regan, 2014). The total breakdown was 34% reporting low activity at Wave 2 (2012-13)
while the other two thirds were evenly split between those reporting moderate or high
activity. Again women were significantly more likely to report low levels of activity (42%)
compared to men (27%), while there was also an age gradient in the numbers becoming less
physically active between waves. However although the proportions remained stable large
numbers of individual participants also transitioned between different levels of activity
(Finucane et al, 2014).
Analysis of the correlates of physical inactivity in older Irish adults found that females were
twice as likely to be inactive as their male counterparts, while those with third level
education, no reported falls in the last year and no fear of falling were more likely to be
active (Murtagh, Murphy, Murphy, Woods, Nevill & Lane, 2015). Poor self-reported health,
not looking after grandchildren, not owning a car and not attending a course were also
associated with inactivity, while among females, living alone or in a rural area and having
poor emotional health or activity-limiting illness were also significantly associated with
inactivity. Among men by contrast, the chief correlates of inactivity were cohabiting,
working and living in an urban area (Murtagh et al, 2015). Another study using TILDA data
found that time spent sitting, mental health, gender and age were the most closely related
to PA, whereas physical health and environment were not (McKee, Kearney and Kenny,
2015).
1.7.2 Mental health in Ireland
Levels of mental distress are high in Ireland, with a recent survey of 7,539 Irish adults finding
that 9% have a ‘probable mental health problem’ and this is significantly more common
amongst women (13%) than men (6%), and amongst older adults aged 65+ (12%) (DoH,
2016, Healthy Ireland Survey 2015). A study using TILDA data found that 10% of the
population aged 50 and over had clinically relevant depressive symptoms (O’Regan, Cronin
and Kenny, 2011), and this was much more prevalent in women than men (12% versus 7%).
However just over a fifth (22%) of this older cohort who were depressed reported a doctor’s
diagnosis suggesting the disease is widely untreated (O’Regan et al, 2011). A further 18% of
the older population meanwhile reported sub-threshold levels of depression.
14
1.7.3 Irish research on link between physical activity and depression in older people
A study by Donoghue et al (2016) found that those who do high levels of PA report better
self-rated health and quality of life and lower levels of depression. It found that 6% of those
doing high level of activity have clinically relevant depressive symptoms compared to 14% of
those doing low or moderate levels and the association was similar for both genders even
though prevalence of symptoms was higher in women. It found that three out of five older
adults reported walking at least 150 minutes and that 13% of those who walked less than
150 minutes a week experienced clinically significant symptoms of depression compared
with 8% in the high walking group (Donoghue et al, 2016). Another study examining the
prevalence of depressive symptoms among a cohort of 2,047 Irish people aged 50-69 found
that those who engaged in four healthy lifestyle behaviours including physical activity, not
smoking, moderate alcohol consumption and eating adequate fruit and vegetables, were
significantly less likely to suffer from depression than those reporting none or just one of
these behaviours, even after adjusting for confounders such as obesity or gender (Maher,
Perry, Perry & Harrington, 2016).
The importance of good mental health to older peoples’ lives was seen in another TILDA
study which showed that it was the most important factor in predicting quality of life ahead
of physical health, social participation, economic resources and socio-demographic status
(Layte et al, 2013). This backs up a previous US study which found PA enhances long-term
quality of life in older people through the mediating influence of its impact on positive
affect, self-efficacy and self-esteem (Elavsky, McAuley, Motl and Konopack, 2005).
1.7.4 Economic cost of poor mental health in Ireland
Poor mental health in Ireland has a high economic cost estimated at €3 billion per annum
making it second only to cardiovascular disease (O’Shea and Kennelly, 2008), yet spending
on mental health is sometimes treated as a low priority area with allocated funds
occasionally diverted for use in other higher priority areas (O’Cionnaith, 2016). Depressive
symptoms are expected to rise in the future given generally increasing rates in the overall
population and the rapidly ageing population (Doyle, Conroy, Hickey & Kelleher, 2014).
15
1.7.5 Research questions
As this review of the evidence shows it is clear there is some inverse association between PA
and depression, but the precise scale and direction of this relationship remain unclear, and
it is uncertain whether high PA leads to better mental health or whether better mental
health leads to higher PA. However with depression prevalent but widely untreated
amongst older Irish adults research into effective methods of treating and preventing it is
crucial to lessen its toll. As no published Irish research has been identified into the
association between changes in physical activity on mental health in later life this study will
aim to explore that link using TILDA data from two waves of data collection.
The specific questions to be addressed are:
1. Who are the people who became more physically active between the two waves of
data collection, and how do they compare with those who remained inactive in
terms of gender, age, marital status and social class?
2. Do older people who increase their level of physical activity experience improved
mental health compared to those who remain or become inactive.
3. Are those who reduce their activity level more likely to see a deterioration in mental
health?
4. If significant associations between changes in physical activity and mental health, do
these persist even when controlling for other variables such as changes in physical
health, widowhood, loss of close friends, changes in socioeconomic status and
changes in quality of social relationships.
The hypothesis of this research is that there is a strong association between increased PA
and improved mental health and if the data supports this, it could provide useful evidence
to support health messages and interventions promoting greater physical activity amongst
older people.
16
Chapter 2: Methodology
2.1 Introduction
This study will examine changes in depressive symptoms amongst older adults against
changes in their level of physical activity to examine if there is an association between
increased activity and improved mental health, and conversely between a more sedentary
lifestyle and worsening mental health, while controlling for other changes in life
circumstances which may be more important in determining outcomes. Secondary
quantitative analysis of a large sample is used because it allows for the systematic analysis
of changes between two time-points of a large and representative cohort. While Singer and
Willett (2003) argue that studies of change based on two waves of data are only marginally
better than one because they cannot establish exactly when changes occurred and can be
biased by measurement error, Newsom (2012) points out that arguments about the
limitations of two-wave data lose the point that two time-points are often all that is
available or practical, and have some advantages over cross-sectional which is also prone to
fallible measurement issues and cannot examine time precedence at all. This chapter
outlines the design and methods employed in The Irish Longitudinal Study on Ageing
(TILDA), ethical issues, a sample profile, the key variables used and derived for this study
and the method of analysis used.
2.2 TILDA
The Irish Longitudinal Study on Ageing (TILDA) is a major inter-institutional research
initiative led by Trinity College Dublin which aims to provide high-quality longitudinal data
relating to older people and the ageing process in Ireland. The sample cohort is of over
8,000 community-dwelling people aged 50 and over and their spouses or partners of any
age. The study collects detailed information on the health, economic, emotional and social
dimensions of participants’ lives every two years and many of the measures used were
designed to be compatible with international studies such as the English Longitudinal Study
17
on Ageing (ELSA) and the Health and Retirement Survey (HRS) in the United States to draw
on best practice and allow for comparisons (Kenny et al, 2010).
Rationale and objective
Although the Irish population remains the youngest in Europe (Eurostat, 2016), it is now
ageing and the proportion of people aged 65 and over is projected to increase from 12.6%
in 2014 to 14% by 2021 and to 19% by 2031 with the greatest increase seen in the oldest old
aged 80 and over (CSO 2004). Population ageing is a worldwide issue with the United
Nations projecting that one in five of the world’s population will be aged over 60 by 2050,
posing massive challenges for the provision of adequate services and healthcare, and
requiring quality research to ensure these needs are properly met (Kenny et al, 2010). The
key objectives of TILDA include generating comprehensive baseline data on older people,
providing new insights into causal processes in ageing; raising the profile of ageing as a
policy issue by disseminating research findings to a wide audience; and encouraging further
Irish and international academic research by making an anonymised dataset openly
available (Kenny et al, 2010). It is funded by the Irish government, Atlantic Philanthropies
and Irish Life and has multidisciplinary input from numerous institutions led by Trinity
College Dublin
2.2.1 Design and Methodology
TILDA is a nationally representative, quantitative longitudinal study designed to cover the
key domains of older people’s lives by taking a multi-disciplinary approach that has drawn
on experts from a wide range of fields to allow for the study of how different domains such
as health, finance and social conditions interact in participants’ lives over time. De Vaus
(2001) highlights the value of longitudinal design in allowing the examination of change or
stability over time, while Bryman (2016) noted that longitudinal studies’ potential for
showing the time order of variables may allow for greater understanding of causal
processes. The TILDA study aims to provide a representative sample of people living in
private households in Ireland aged 50 years or over as well as information about their
spouse or partner of any age. Participants who enter into institutional settings such as
nursing homes subsequent to the first interview are also followed over time.
18
Sampling
The first wave of the TILDA study was sampled using a clustered sampling frame based on
the GEO directory, a database of postal addresses developed and held by An Post. This
sampling frame was chosen as it had been widely used and had well-known properties. The
Department of Social Protection’s register of Personal Public Service Numbers was a
possible alternative, but this possibility was discounted after an initial evaluation suggested
a significant mismatch between it and the number of individuals listed in the National
Census (Kenny et al, 2010).
A pilot study was conducted to provide evidence on the likely levels of non-eligibility in the
sampling frame through vacant housing or the absence of a person in the right agegroup. It
could also provide an initial assessment of the likely response rates. The pilot indicated
around half of the addresses would be ineligible because there would be nobody aged 50 or
over, while the target response rate of eligible households was 60 percent. Statistical and
resource factors determined a minimum required sample size of 8,000 persons for the
TILDA baseline survey (Kenny et al, 2010).
A two-stage sample design was developed, using the ESRI’s RANSAM software which first
grouped addresses into 3,155 clusters with between 500 and 1180 addresses each, from
which 640 clusters were randomly selected, after prior proportionate stratification by socioeconomic group and location based on Central Statistics Office Small Area Population
statistics (Kenny et al, 2010). Probability samples of 50 addresses in each cluster were then
selected and a sample list of 25,600 addresses were randomly selected, from which the
target sample was approached by fieldworkers. This sample design using stratification,
clustering and multi-stage selection was designed to be “epsem” and self-weighting, except
for biases caused by non-random response rate variation which can be dealt with at analysis
stage by use of population weights (Kenny et al, 2010).
Data collection
TILDA began collecting data from participants between 2009 and 2011 followed by a second
wave in 2012-13, a third in 2014-15 and a fourth wave in 2016. Three methods of data
collection were used; a Computer-Aided Personal Interview (CAPI) and a Self-Completion
Questionnaire (SCQ) at each wave; and a Health Assessment in Waves 1 and 3. Pilot studies
19
indicated problems with response rates, so publicity campaigns were held and an incentive
payment of €20 was introduced to encourage greater participation.
The CAPI was carried out by trained interviewers at the participants’ homes. The
questionnaire took about 90 minutes to complete and included detailed information on
demographics, social and family circumstances, health and healthcare use, employment
history, income, assets and transport, with extensive relevant routing of questions
dependent on initial answers. This study primarily used variables captured from CAPI.
The SCQ is a paper and pen based survey with the questionnaire left at participants’ homes
after the CAPI has been completed. The SCQ was designed to take 20 minutes to complete
and included detailed questions on sensitive topics such as sexual activity, alcohol intake
and anxiety.
Health assessments on participants were carried out in Wave 1 and Wave 3. These included
cognitive, cardiovascular, vision, gait and mobility, bone and muscle strength and blood
tests. 72% of participants completed a health assessment at Wave 1, the large majority of
which were carried out in dedicated centres in Dublin and Cork, with around 14% of this
cohort having a modified home-based assessment (Cronin, O’Regan, Finucane, Kearney and
Kenny, 2013).
Response Rate
The first wave of data was collected between 2009 and 2011 and had a 62% response rate
collecting data from 8,504 individuals, which included 8,175 participants aged 50 and over,
and 329 younger spouses or partners. The second wave of data was collected between 2012
and 2013 with an 86% response rate, in which 6995 out of the 8175 original Wave 1
respondents completed an interview. There were also 170 interviews with new respondents
which included both people invited to take part in Wave 1 who had declined to participate
then, and new spouses or partners of core respondents (Dooley, 2014), and there were also
155 end-of-life interviews with relatives or carers of those who had died since Wave 1.
Neither of these categories will be included in this analysis and nor will interviews given by
proxy.
20
2.2.2 Ethics and Access
TILDA has received ethical approval from Trinity College Dublin Ethics Committee for each
wave of data collection, with special attention paid to the need to protect participants’
anonymity and confidentiality, particularly in light of the sensitive financial, health and
social information provided. Informed consent was obtained from participants at the time
of each CAPI and health assessment including the right to withdraw at any time. The TILDA
study took special care in its treatment of vulnerable individuals such as those with agerelated disabilities, and rigorous data protection safeguards were put in place.
This study accessed TILDA data directly at the TILDA offices in Trinity College Dublin where
the researcher was employed during completion of the dissertation. While it was originally
intended to utilise the public TILDA dataset which had been accessed via the Irish Social
Science Data Archive in University College Dublin, a number of crucial variables including
marital status at Wave 2 and attrition weights, were not available on this, meaning the full
research dataset was required to meet the objectives of the present study. Access to the full
research database for the purposes of this study was approved by the TILDA Management
Committee on 8th July 2016 and full training in data protection was provided. Access to the
dataset was only permitted in the TILDA offices, and only results and outputs could be
exported from the office. No export of raw data was permitted, and measures were in place
to screen email to prevent data breaches.
Ethical approval for this secondary analysis study was granted by the Trinity College Dublin
Research Ethics Committee on 9th June 2016.
2.3 Weights
As the research questions of this study are concerned with the relationship between
changes in physical activity and depressive symptoms over time it was important to adjust
for bias caused by non-response from particular sub-groups, both in the original sample and
as a result of attrition in participation between waves.
21
TILDA provides cross-sectional weights for each wave of data to allow adjustment for
differences between the age, sex and educational profile of the sample compared to the
proportions found in the general population as measured by the Census 2011. The main
CAPI cross-sectional weight is used for inferential analysis from the Wave 1 data. This
ensures that estimates from different subgroups are proportionate to their actual size in the
population (Dooley, 2014). When it came to analysing Wave 2 and to the core analysis of
change over time it was crucial to use an attrition weight that adjusted for the original
sample bias and further corrected for the increased likelihood of participants from some
subgroups to drop out. Attrition is an important problem in longitudinal analysis that affects
sample size and compromises estimates of population parameters.
As outlined, 6995 of the original 8175 participants aged 50 and over at Wave 1 completed
an interview at Wave 2, with 1180 failing to do so. The reasons for non-response are
outlined in Table 2.1.
Table 2.2 Reason for non-participation at Wave 2
Reason
Number
In 205 cases the participant was deceased,
Deceased
205
while the 166 lost to follow-up had mainly
Lost to follow-up
166
Refusal
809
Total
1180
moved outside Ireland or were
uncontactable within Ireland (Dooley,
2014). Previous analysis showed that some
Wave 1 participants were less likely to participate in Wave 2 than others and this was nonrandom with factors affecting attrition including measures of cognitive and behavioural
health, marital status and several health measures (Dooley, 2014). Differences between
those who remained and those who left the survey are outlined in the sample profile in
Section 2.4 The CAPI attrition weight used to adjust for differences was for survivors who
completed a non-proxy interview based on the reciprocal of the probability of a Wave 1
respondent taking part in Wave 2.
22
2.4 Sample profile
This study analysed a sample of 8175 participants aged 50 and over at Wave 1. Some 6995
of these participants remained at Wave 2 with reasons for non-response by a subgroup of
1080 outlined in Table 2.1 above. A subset of 6871 people who reported their level of
physical activity at both waves of data collection will be analysed at Wave 2 and for the
longitudinal analysis of changes between waves as seen in Figure 2.1 below:
Figure 2.1 Study sample from TILDA
8175
aged 50+ at
Wave 1
•329
spouses/partners
aged <50
excluded
6995
left in Wave 2
•1080 did not
participate in
Wave 2
6871
final sample
•Reported level
of physical
activity at
both waves
Given the high level of attrition between waves it was important to look at the profile of
participants at each wave and those who left. The results shown in Table 2.2 highlight
important differences between the groups. This reveals large difference between the
subgroups as those who left were older, less educated and more likely to be disabled or
retired than those who remained. For example, 42.2% of those who left had only a primary
education compared to 28.7% amongst the subgroup who remained in the survey, while
21.4% of attriters had a tertiary education compared to 30.8% of those who remained. A
quarter of those who left the survey were aged 75 or over at baseline, compared to 14.9%
of those who remained, a difference which would affect the health and educational profile
23
of the subgroups. Importantly for the present study the level of depressive symptoms was
also higher amongst the attrition group with 11.1% reporting severe symptoms compared to
9.4% of those who remained in the survey. These differences are an important source of
potential bias in the study and the method of addressing it was discussed under the weights
section.
24
Table 3.2: Profile of participants at Wave 1, Wave 2 and those who dropped out after Wave 1.
Wave 1
Total
Wave 2
Total
People who
left after
Wave 1
N=1180
N=8175
%
N=6995
%
4668
2163
1344
63.94
(9.79)
57.1
26.5
16.4
4093
1859
1043
63.47 (9.52)
58.5
26.6
14.9
575
304
301
66.01 (11.01)
48.7
25.8
25.5
3744
4431
45.8
54.2
3197
3798
45.7
54.3
547
633
46.4
53.6
Highest education
Primary or none
Secondary
Tertiary
2504
3263
2404
30.6
39.9
29.4
2007
2835
2152
28.7
40.5
30.8
497
428
252
42.2
36.4
21.4
Labour market
status
Retired
Employed
Other
3046
2934
2195
37.3
35.9
26.9
2593
2553
1849
36.5
37.1
26.4
493
341
346
41.8
28.9
26.4
Location
Dublin
Other town/city
Rural
1936
2311
3916
23.7
28.3
48.0
1663
1985
3337
23.8
28.4
47.8
273
326
579
23.2
27.7
49.2
Disability
ADL and/or IADL
Not disabled
Disabled
7189
986
87.9
12.1
6191
804
88.5
11.5
998
182
84.6
15.4
Depressive
symptoms
None/mild
Moderate
Severe
5851
1417
776
72.7
17.3
9.5
5047
1201
648
73.2
17.4
9.4
804
216
128
70.0
18.8
11.1
Age group
50-64
65-74
75+
Mean age at Wave
1 (standard
deviation)
Sex
Male
Female
%
Physical activity
Low
2592
32.0
2165
31.3
427
36.6
Medium
2787
34.4
2388
34.5
399
34.2
High
2717
33.6
2375
34.3
342
29.3
Missing values: Wave 1: Education 4; Location 12; Physical activity 79. Wave 2: Location 9; Education 2.
Physical activity 63.
25
In general the original TILDA sample was slightly younger than that found in Census 2011,
and had slightly more women and married participants, all of which was corrected for by the
sample weights.
2.5 Variables for analysis
This section outlines the main measures used in this analysis which includes variables from
the original TILDA dataset and those derived for this study. It is broken down into
dependent variables, independent variables and control variables.
2.5.1 Dependent Variables
Mental Health
To assess changes in mental health, two measures from TILDA were used: depressive
symptoms in past week and self-ratings for emotional and mental wellbeing. These variables
were used in a previous TILDA study exploring changes in parents’ mental health after the
emigration of adult children (Mosca and Barrett, 2014).
The depressive symptoms measure used was the 20-item Centre for Epidemiologic Studies
Depression (CES-D) scale which asks respondents about the extent to which they have
experienced depressive symptoms in the past week. The CES-D is a widely-used self-report
scale which has found to be reliable across racial, gender and age categories and has strong
internal consistency with Cronbach’s alpha coefficients ranging from 0.85 to 0.90 (Radloff,
1977, midss.org 2016). It has also demonstrated strong concurrent and construct validity
(Radloff, 1977). However although there is some evidence that the CES-D may not be a good
tool for screening for major depression (Roberts, Vernon and Rhoades, 1989), a later study
found it was robust as a screener for depression amongst community-dwelling elderly
people (Lewinsohn et al 1997). CES-D was also used in a UK study into the health benefits of
taking up physical activity in later life (Hamer et al. 2014). A reliability analysis of the scale
was carried out for this sample and showed it had a high Cronbach’s alpha value of 0.93
while the corrected item-total correlations were all well above 0.3. This indicated the scale
was a highly reliable measure of depressive symptoms.
CES-D has questions on negative feelings such as feeling sad or having crying spells; positive
feelings such as enjoying life; psychosomatic symptoms such as insomnia and loss of
26
appetite, and social relations such as finding others unfriendly. Each question is measured
on a 4-point scale based on whether the respondent has experienced these symptoms
rarely/none of the time, some of the time, most of the time or all of the time. There is a
maximum score of 60 with a cutoff point of ≥16 to define clinically significant symptoms of
depression (American Psychological Association [APA] n.d.). The CES-D score at Wave 1 will
be subtracted from the score at Wave 2 to determine if depressive symptoms have
changed, with a positive number indicating that they have increased (i.e. a deterioration in
mental health) and a negative number indicating fewer depressive symptoms (i.e. an
improvement). The CES-D measure also allowed for analysis of how many participants in
each activity group suffered clinical symptoms of depression at the ≥16 level at both waves
and sub-threshold symptom levels of depression which have been defined in previous
literature as 8-15 symptoms on the CES-D scale (O’Regan, Cronin and Kenny, 2011) with a
separate depressive symptoms variable based on this categorising people into 3 groups,
those with “none or mild” symptoms, those with “moderate” symptoms and those with
“severe” symptoms. The full text of the 20-item CES-D questionnaire is included in Appendix
2.
Self-rated mental health scores will also be examined. Participants were asked to rank their
emotional or mental health as excellent (1), very good (2), good (3), fair (4) or poor (5), so
the scores in Wave 1 will again be subtracted from those in Wave 2, with positive values
indicating a disimprovement since the first interview and negative scores indicating
improved self-rated mental health scores. A breakdown of the average mental health scores
on these measures of each activity group at Wave 1 and Wave 2 will be obtained and the
relevant changes in each group highlighted to see how they differ.
A Canadian study found a correlation between poor self-rated mental health scores and
mental morbidity, although it was not a substitute for specific mental health measures
(Mawani and Gilmour, 2010) and was secondary to the CES-D measure in this study.
2.5.2 Independent Variables
Physical Activity
27
The main independent variable being examined is change in level of physical activity
between Wave 1 and Wave 2. The key TILDA variables used to derive this measure were
themselves derived from the short form International Physical Activity Questionnaire (IPAQ)
which asks people about the amount of time they spent being physically active in the past
seven days categorised into high, moderate and low levels of activity (Kenny, 2010). These
include the frequency and duration of time spent doing vigorous activities such as fast
cycling or heavy digging which result in breathing much faster than normal; moderate
activities requiring moderate effort such as doubles tennis or carrying light loads; and the
frequency and duration of walking. This is the short form of the IPAQ which has been
internationally validated and found to have acceptable measurement properties (Craig et al.
2003), although it was primarily developed for use in adults aged 15-69 years (IPAQ, 2004).
The time spent on each activity is weighted based on its energy requirement giving a total
score in MET-minutes – i.e. the metabolic energy expended on these activities multiplied by
the amount of time spent doing them – to give a weekly MET-minutes score for each
participant. Each participant can then be classified as having High, Moderate or Low Activity
based on the criteria outlined below (IPAQ, 2004; Donoghue et al, 2016).
Table 2.3. Physical activity classifications.
High activity
(either one of these 2
criteria)
Moderate activity
(any one of following 3
criteria)
Low Activity
Vigorous activity on 3 or more of last 7 days, accumulating
at least 1500 MET-minutes per week
OR
Any combination of walking, moderate or vigorous activities
accumulating at least 3000 MET-minutes/week
Vigorous activity of at least 20 minutes on 3 or more
day/week
OR
Moderate activity of at least 30 minutes on 5 or more days
per week
OR
Any combination of walking, moderate or vigorous activities
on 5 or more days accumulating at least 600 METminutes/week
Meets none of the criteria for high or moderate activity
The IPAQ questions were asked during the CAPI and the full IPAQ short-form questionnaire
is included in Appendix 1.
28
For the purposes of this study dichotomous variables at each wave coded 0 for ‘ low activity’
and 1 for ‘moderate/high activity’ were also created. This simplified the creation of change
variables looking at how participants’ activity levels changed between the two waves of
data-collection. This new change variable created the following four mutually exclusive
subgroups (unweighted):
Number
Percent
Remained inactive
1220
17.8
Became inactive
1052
25.3
Became active
930
13.5
Remained active
3669
53.4
The size of each subgroup was big enough to allow sufficient statistical power for
comparisons on a range of different variables.
2.5.3 Control variables
Previous research has indicated a number of factors that can contribute to changes in
depressive symptoms in later life including changes in health, disability and marital status
(Choi and Bohman, 2007) and changes in income and employment status (Tiedt, 2013) and
it was necessary to control for these to identify the degree to which changes in any of them
may have impacted on changes in depressive symptoms between waves more than the
physical activity changes being examined.
Physical health variables
New chronic illness, disability and poor self-rated health have been found to be risk factors
for depression in later life (Choi and Bohman, 2007) and TILDA includes a wide range of
questions on these, permitting changes in health status between Wave 1 and Wave 2 be
analysed as in previous TILDA research (Mosca and Barrett, 2016).
New illness
As a health assessment was not carried out in Wave 2, self-reported variables from the CAPI
were used to assess doctor-diagnosed onset of cardiovascular conditions and other chronic
conditions since the first wave of data collection. Participants were asked about new
29
diagnoses of high blood pressure, diabetes, high cholesterol, heart attack, angina,
congestive heart failure and stroke with a variable to indicate if they have been diagnosed
with one or more of these since Wave 1.
They were also asked about the onset of new illnesses comprising lung disease, asthma,
arthritis, osteoporosis, cancer, liver disease, Parkinson’s and Alzheimer’s disease since their
first interview and this allowed creation of a new dummy variable for onset of chronic
illness, with those reporting one or more new condition coded 1 and those reporting no
new condition coded 0.
Physical functioning
The ability to carry out everyday tasks is an important facet of healthy ageing that can have
a bearing on mental health (Choi and Bohman, 2007), and these are assessed by variables
measuring the difficulties recorded in activities of daily living (ADLs) and difficulties with
instrumental activities of daily living (IADLs). ADLs include basic task such as dressing, eating
and using the toilet while IADLs are activities which allow people live independently in the
community such as grocery shopping, preparing a meal or managing money. A deterioration
or improvement in participants’ functional capacity can be assessed by measuring changes
in the number of difficulties with ADLs and IADLs between the two waves, and two new
variables were derived to establish if people had acquired one or more ADLs and one or
more IADLs since Wave 1.
Self-rated health
Participants were also asked to rate their health (compared to others their age) on a fivepoint scale as ‘excellent’ ‘very good’, ‘good’, ‘fair’ or ‘poor’ and by subtracting their wave 1
score from their wave 2 score, a measure of the change in this can be found, with a positive
number indicating a worsening of self-rated health. As in Mosca and Barrett (2016) 1-point
and 2-point changes were used as variables to indicate a change in health.
Bereavement
Loss of a spouse can have a major bearing on people’s mental health (Choi and Bohman,
2007) so a variable for this was derived to use as a regressor. Separately the loss of close
friends/relatives was measured by subtracting the number of such close relationships at
30
wave 2 from the number at wave 1, with a positive number indicating a loss. A dummy
variable was then created to represent those who had lost 1 or more such close
relationships over the period.
Economic
Changes in labour market status can also affect mental health (Tiedt, 2013) so two new
dummy variables to capture whether participants had retired or become unemployed (or
employed) were included as regressors. A change in income was considered as a possible
regressor but due to differences in how household and individual income was measured
between the two waves it was not possible to construct a viable measure of this for the
purposes of this study. However a previous study has indicated that the gross income levels
of TILDA participants remained the same over the period, although their wealth declined
due to falling property prices (Hudson, Mosca and O’Sullivan, 2014). The relative stability of
income for this age cohort could of course conceal important individual fluctuations in
income, especially as this was a period of severe economic recession in Ireland.
Other variables
Basic demographic variables including gender, age, location (Dublin, other town or city,
rural), highest educational level (primary, secondary, tertiary) are also included in the
analysis.
2.6 Data analysis
Data were analysed using SPSS 22 with variables examined for missing values, outliers,
normality of distribution, homoscedasticity and multicollinearity. In a small number of cases
(n=10) missing values on age were identified and added from the more recently updated
Stata dataset. The significance level for findings was set at p<.05 in keeping with the
literature, with measures of effect size also reported where relevant to distinguish between
trivial and important findings (Tabachnik and Fidell, 2007). Analysis will be carried out in
three stages as outlined below
2.6.1. Wave- specific analysis
Firstly the subgroups reporting different levels of physical activity at Wave 1 and at Wave 2
were profiled in terms of their demographic breakdown and depressive symptoms
31
separately for each wave. The relevant CAPI weight will be applied for inferential statistics.
One-way Anovas will be used to compare means on level of depressive symptoms, with the
relevant post-hoc comparisons (Tukey/Games-Howell) for statistically significant differences
depending on Levene’s test for homogeneity of variance.
Stage 2. Changes in activity and mental health.
The second part of the analysis involved analysing the level of change in depressive
symptoms and self-reported mental health for the four physical activity groups identified
earlier; “remained inactive”, “became active” “remained active” and “became inactive”.
One-way ANOVAs and paired sample T-tests were used to compare means between the
subgroups with the relevant post-hoc comparisons.
Stage 3 analysis. Controlling for other factors
The third part of the analysis involved examining whether changes in mental health for the
different activity groups persisted when controlling for other social, health and economic
factors that might influence this. This included changes in physical health, changes in
physical functioning, bereavement of spouse and loss of close friends/relatives, and changes
in economic status. An ordinary least squares regression model was built to examine the
impact of changes in physical activity on changes in depressive symptoms with dummy
variables created to allow comparisons of the different activity change groups. The
dependent variable was the change score computed by subtracting the CES-D depression
score at Wave 1 from the score at Wave 2. Newsom (2012) points out that this type of
“difference score” approach is one of two common approaches to predicting change over
two time points, the other being lagged regression where the value of the dependent
variable at follow-up is also regressed on its value at baseline. He notes there has been
fervent debate about which approach is superior, but the difference score approach is more
targeted at individual differences in change, whereas the lagged regression approach is
more concerned with average change. He notes there is general consensus now that
“difference scores are not pure evil, nor are they any more of a magical solution than the
lagged regression approach,” (Newsom, 2012: 161).
32
Chapter 3: Findings
3.1 Introduction
The aim of this research is to establish if becoming more physically active later in life is
associated with improved mental health and if any differences persist when confounding
factors such as bereavement, retirement, illness and disability are also controlled for. The
analysis and findings are organised into three sections to address the central issues:
Section 1. Wave-specific analysis
1. How do the subgroups reporting different levels of physical activity differ from each other
demographically at both Wave 1 and Wave 2?
2. Do the subgroups reporting different levels of physical activity differ from each other in
terms of mental health at Wave 1 and Wave 2?
Section 2. Changes in activity and mental health
1. Do older people who increase their level of physical activity experience improved mental
health compared to those who remain or become inactive?
2. Are those who reduce their activity level more likely to experience a deterioration in their
mental health?
Section 3. Changes in mental health controlling for other factors
1. Do any changes in mental health persist after controlling for other factors which may have
changed over the period such as bereavement, retirement, unemployment, new illness and
disability?
33
3.2 Wave specific analysis
This section will look at demographic differences between different physical activity groups
at Wave 1 and Wave 2 and then explore differences in mental health between the groups at
each wave.
3.2.1 Demographic differences
Wave 1
People who were most physically active were more likely to be younger, male, bettereducated, married, working and enjoy better self-rated health than the less active groups.
As shown in Table 3.1 the high activity group was younger on average (M=61.28, SD=8.37)
than the low activity group (M=65.95, SD=10.69) and the medium activity group (M=63.48,
SD=9.42). Similarly 71.7% of the high activity group was married compared to 59.1% and
66.0% of the low and medium activity groups respectively while two thirds of the high
activity group had a secondary or third-level education compared to 54.9% of the low
activity group. Nearly half the high activity group meanwhile was working (47.9%) compared
to under a quarter (24.7%) of the low activity group and a third of the medium activity
group (33.2%). The high activity group was also more likely to be unemployed (6.3%) than
the low activity group (4.3%), but this may reflect the fact that substantially more of them
were still in the labour force rather than retired or otherwise disengaged from it. There was
a big gender disparity on activity levels with 59.8% of the most active group being men
whereas 61.5% of the least active group were female and this might explain some of the
other demographic variation seen in terms of age, health and employment status. When it
came to health over a quarter (27.3%) of the least active group reported fair or poor health
whereas fewer than one in 10 of the more active group put themselves in this category
(8.5%) and 14.5% of the medium activity group. Location did not appear to have much
bearing on activity levels as there was no consistent gradation seen between urban, rural
and Dublin dwellers in terms of activity.
34
Table 3.1 Characteristics of different activity groups Wave 1
Wave 1
Low activity
%
Medium activity
%
High activity
%
48.5
23.3
28.2
65.95 (10.69)
57.7
26.1
16.2
63.48 (9.42)
68.5
21.8
9.7
61.28 (8.37)
38.5
61.5
45.6
54.4
59.8
40.2
59.1
12.7
6.5
66.0
12.1
6.5
71.7
11.3
6.7
21.7
15.4
10.3
Employment status
Retired
Employed
Unemployed
Other
40.4
24.7
4.7
30.2
38.8
33.2
5.4
22.6
29.7
47.9
6.3
16.1
Location
Dublin city or county
Other town or city
Rural
24.8
30.4
44.9
30.0
31.8
38.2
23.9
28.8
47.3
Highest education
Primary or none
Secondary
Tertiary
45.1
40.4
14.5
37.3
42.2
20.5
32.3
46.9
20.7
Self-rated health
Excellent/v.good/good 72.7
Fair/poor
27.3
85.5
14.5
91.5
8.5
Total
100.0
100.0
Age group
50-64
65-74
75+
Mean age (sd)
Sex
Male
Female
Marital status
Married
Never married
Separated/
Divorced
Widowed
100.0
N=8175. Missing obs Activity level 87. Location 12. Education 4. Self-rated health 19
35
Wave 2
A similar pattern was seen at Wave 2 as people who were most physically active tended to
be younger, male, better educated, married, working and rated their health better than the
less active groups. As shown in Table 3.2 overleaf, the most active group was younger on
average (M=62.89, SD=8.42) than the low activity group (M=68.55, M=10.97) or the medium
activity group (M=65.20, SD=9.61). The high activity group was also more likely to be
married and only half as likely to be widowed at 9.1% as compared to 21.1% among the low
activity group. All activity groups had seen an increase in the proportions retired and
decrease in those employed which probably reflects the slightly older age profile. Self-rated
health had disimproved for all groups but the pattern remained similar with 32.0% of the
low activity group reporting fair or poor health compared to 11.6% of the high activity
group.
36
Table 3.2 Characteristics of different activity groups at Wave 2
Wave 2
Low activity
Percentage
Medium activity
Percentage
High activity
Percentage
Age group
50-64
65-74
75+
Mean age (sd)
41.9
26.4
31.6
68.55 (10.96)
52.7
28.7
18.6
65.20 (9.61)
62.9
26.4
10.7
62.89 (8.42)
Sex
Male
Female
38.4
61.6
47.1
52.9
59.7
40.3
59.6
11.1
8.1
62.6
13.8
9.4
69.3
13.5
8.0
21.1
14.2
9.1
Employment status
Retired
Employed
Unemployed
Other
44.8
22.1
4.2
28.9
42.8
30.7
5.5
21.1
34.7
45.6
5.7
14.0
Location
Dublin city or county
Other town or city
Rural
27.5
29.2
43.3
29.1
32.1
38.9
20.0
31.1
48.9
Highest education
Primary or none
Secondary
Tertiary
37.8
43.4
18.8
28.1
45.5
26.4
26.3
47.3
26.3
Self-rated health
Excellent/v.good/good 68.0
Fair/poor
32.0
84.0
16.0
88.4
11.6
Total
100.0
100.0
Marital status
Married
Never married
Separated/
Divorced
Widowed
N=6871. Missing obs
37
100.0
3.2.2 Mental health of different activity groups
Depressive categories Wave 1
A cross-tabulation of activity levels and depressive symptoms at Wave 1 classified into
‘none/mild’, ‘moderate’ and ‘severe’ showed a gradient between physical activity and
depressive category. As seem in Table 3.4 it showed that the low activity group was more
than twice as likely to report severe depressive symptoms (14.7%) as the high activity group
(6.6%) and compared to 9.3% of the medium activity group as shown in Table 3.3. The
proportion of participants reporting no or mild depressive symptoms increased from 63.1%
of the low activity participants to 78.0% of the most active group. High activity participants
were also far less likely to report moderate symptoms of depression (15.4%) than low
activity participants (22.1%) These differences were found to be significant [χ2 (4)=168.68,
p<.001] but the Cramer’s V measure of effect size was 0.103 which Cohen’s guidelines
(1988) indicate is a small effect.
Table 3.3. Cross tabulation of physical activity and depressive symptoms at Wave 1 with column percentages
Wave 1
Depressive
symptoms
None/mild
Moderate
Severe
Total
Low activity
Medium activity
High activity
% of sample total
63.1%
22.1%
14.7%
100.0%
74.1%
16.6%
9.3%
100.0%
78.0%
15.4%
6.6%
100.0%
71.8%
18.0%
10.2%
100.0%
N=8175. Missing obs. 217.
Depressive symptoms (CES-D) Wave 1
A one-way between-groups analysis of variance was carried out to explore the relationship
between physical activity and depressive symptoms as measured by the CES-D scale. As
Levene’s test showed that homogeneity of variance between the groups was violated,
Welch’s F was used as a more robust alternative to ANOVA (Field, 2013). This showed
significant differences in average depressive symptoms between the different physical
activity groups with those reporting more physical activity also reporting fewer depressive
symptoms [F(2,7954)=96.31, p<.001]. However, despite reaching statistical significance, the
actual difference in mean depression score between the groups was quite small according
38
to Cohen’s guidelines (1988), as the effect size calculated using eta squared was .024. Posthoc comparisons using the Games-Howell test indicated that the average depression score
for the group reporting low activity (M=7.62 , SD=8.27 ) was significantly higher than for
those reporting medium activity (M=5.71, SD=6.98) or high activity (M=4.90 , SD= 6.51). The
mean difference between the medium and high activity groups was also significant. This
indicates those doing more physical activity enjoy better mental health than those who are
less active.
Self-rated emotional/mental health Wave 1
Turning to self-rated emotional or mental health there was also a clear gradation in ratings,
with the proportions at Wave 1 who gave themselves a positive rating of excellent, very
good or good, ranging from 86.2% of the low activity group to 92.0% of the high activity
group as seen in Table 3.4, and a similar gradient for more negative assessments with 13.8%
of the low activity group having a fair or poor self-rating compared to 8.0% of the high
activity group. These differences were found to be statistically significant [χ2 (2)=7942.62,
p<.001] but the Cramer’s V measure of effect size was 0.06 which Cohen’s guidelines (1988)
indicate is a small effect.
Table 4.4 Cross-tabulation of activity level and self-rated emotional/mental health
Low activity
Medium activity
High activity
% of Total
Excellent/very good/good
86.2%
90.8%
92.0%
89.7%
Fair/poor
13.8%
9.2%
8.0%
10.3%
Wave 1
Depressive categories Wave 2
Turning to Wave 2, a cross-tabulation of activity levels and depressive categories again
showed a strong gradient between activity level and depressive symptoms with the most
active group reporting fewer depressive symptoms. The low activity group was again over
twice as likely to report severe symptoms (14.0%) as the high activity group (5.8%) while the
proportions reporting no or mild depressive symptoms rose from 66.9% amongst the least
active group to 82.0% amongst the most active. These differences were found to be
statistically significant [χ2 (4)=161.01, p<.001] but the Cramer’s V measure of effect size was
39
0.109 which Cohen’s guidelines (1988) indicate is a small effect. This suggests a small
association between a person’s activity level and their depressive symptoms.
Table 3.5 Cross-tabulation of activity level and depressive categories at Wave 2. Column percentages
Wave 2 depressive
Low activity
Medium activity
High activity
% of Total
None/mild
66.9%
74.6%
82.0%
74.4%
Moderate
19.1%
17.8%
12.2%
16.4%
Severe
14.0%
7.6%
5.8%
9.1%
Total
100.0%
100.0%
100.0%
100.0%
symptoms
N=6871. Missing observations 105
Depressive symptoms CES-D Wave 2.
A one-way between groups analysis of variance was carried out to explore the relationship
between physical activity and depressive symptoms at Wave 2. Welch’s F was used as a
more robust alternative to ANOVA. This showed statistically significant differences in
average depressive symptoms between the different groups with those reporting more
physical activity also reporting fewer depressive symptoms [F(2,5538.44)=94.233, p<.001].
But despite reaching statistical significance the difference in mean depressive symptoms
between the groups was quite small according to Cohen’s guidelines (1988), as the effect
size calculated using eta squared was 0.029. Post-hoc comparisons using the Games-Howell
test indicated that the average depression score for the group reporting low activity
(M=7.20, SD=8.44) was significantly higher than for those reporting moderate activity
(M=5.34, SD=6.52) or high activity (M=4.20, SD=5.92).
Self-rated emotional/mental health Wave 2
The pattern seen at Wave 2 was almost identical to Wave 1, with the proportions rating
their own mental health highly rising from 83.3% to to 92.7% in line with activity levels. As
seen in Table 3.6 those in the low activity group were over twice as likely to report fair or
poor mental health as those in the high activity group (16.7% compared to 7.3%). The
40
differences between the activity groups were found to be statistically significant [χ 2
(2)=108.06, p<.001] but the Cramer’s V measure of effect size was 0.125 which Cohen’s
guidelines (1988) indicate is a small effect. This suggests a small association between
physical activity and self-rated emotional or mental health. Overall the proportion rating
their mental health as excellent/very good/good fell slightly at Wave 2 compared to Wave 1.
Table 3.6 Crosstabulation of physical activity level and self-rated emotional/mental health at Wave 2
Wave 2 Self-rated
Low
Medium
High
Total %
Excellent/very good/good
83.3%
90.3%
92.7%
88.7%
Fair/poor
16.7%
9.7%
7.3%
11.3%
emotional/mental health
N=6871. Missing observations 2.
3.2.3 Section 1 Summary
Do the subgroups reporting different levels of physical activity differ from each
other demographically?
Yes. At both waves of data collection the most active group is younger, and more
likely to be male, better-educated, married, working and to report better selfrated physical health.
Do the subgroups reporting different levels of physical activity differ from each
other in terms of mental health?
Yes. People in the most physically active group were half as likely to report
clinically significant symptoms of depression as those in the low activity group, and
were more likely to report no/mild depressive symptoms. There was a clear
gradient with the level of depressive symptoms increasing in inverse relationship
to activity level and this pattern was seen at both waves of data collection. A
similar pattern was observed for self-rated emotional or mental health with the
more active groups having more positive self-assessments at both timepoints.
41
3.3 Section 2. Changes in activity and mental health
This section explored the association between changes in physical activity and changes in
mental health between the two waves of data collection to examine if older people who
increase their level of physical activity experience improved mental health, and conversely
whether those who reduce their activity level suffer a deterioration in their mental health.
3.3.1 Changes in physical activity between waves
The proportion of participants reporting low, medium and high activity levels remained
broadly similar to Wave 1 at around a third each. However many individuals within those
groups transitioned between different activity levels as shown in Table 3.7.
Table3.7. Crosstabulation between activity level at Wave 1 and Wave 2 with column percentages
Changes between
W1 Low
W1 Medium
W1 High activity
Total
waves
activity
activity
W2 Low activity
57.4%
27.8%
16.9.0%
33.7%
W2 Medium
25.6%
46.2%
30.8%
34.4%
W2 High activity
17.0%
26.0%
52.3%
31.9%
Total
32.1%
34.4%
33.5%
100.0%
activity
N=6871.
While over half (57.4%) of participants reporting low activity at Wave 1 remained in this
category at Wave 2, the remainder tran56sitioned to either medium (25.6%) or high levels
of activity (17.0%). Amongst the medium activity group 46.2% retained this level of activity
while over half became either more or less active. Over half the high activity group
remained in this category at Wave 2, but 16.9% transitioned to low activity and 30.8%
reported medium activity at Wave 2. It should be noted that the methodology for assessing
physical activity was unchanged between waves. As discussed in the methodology section
four groups were created categorising participants into the following four groups depending
on whether they retained or changed their activity level. The breakdown of these groups is
given in Table 3.8.
42
Table 3.8. Breakdown of activity change groups (weighted)
Activity change groups
% (N=6871)
95% CI
Remained inactive
18.4%
17.5-19.3
Became inactive
15.2%
16.1-17.1
Became active
13.7%
12.9-14.5
Remained active
52.6%
51.4-53.6
To explore a possible reason for the large amount of transition the timing of Wave 1 and
Wave 2 interviews was analysed by recoding CAPI date variables to capture whether these
were held in winter (October-March) or summer (April-September) and this showed a very
marked difference with an almost even split (50.2% winter, 49.8% summer) for the timing of
Wave 1 interviews, whereas 84.0% of Wave 2 interviews were held in summer. With
previous research indicating that physical activity in older people is higher during summer
months (Berger, Mutrie, Hannah and Der, 2000), this could help explain a sizable cohort
increasing their activity levels, though against this, an even larger cohort transitioned from
high/medium activity to low activity.
Figure 3.1 Season of Wave 1 interview
60.0%
50.0%
52.8%
47.2%
50.7%49.3%
Stayed inactive
Became inactive
53.1%
46.9%
51.7%
48.3%
40.0%
30.0%
20.0%
10.0%
0.0%
Winter
43
Became active
Summer
Remained active
Figure 3.2. Season of Wave 2 interview
90.00%
81.90%
84.90%
81.30%
85.20%
80.00%
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
18.10%
18.70%
15.10%
14.80%
10.00%
0.00%
Stayed inactive
Became inactive
Winter
Became active
Remained active
Summer
Subgroup profiles
A comparison of the groups who remained or became inactive against those who remained
or became active again showed that the more active ones tended to be younger, better
educated and were more likely to be working, married and enjoying good health. However,
although men dominated in the remained active group, the opposite was seen in the newly
active group where women comprised 55.8% of the total. This was a change from the wavespecific analysis where men outnumbered women at the higher activity levels. This newly
active group also had a higher proportion of employed people than the group who
remained or became inactive as seen in Table 3.8. There was a slight urban/rural split as the
newly active group had the highest proportion of rural dwellers (45.8%) and the lowest
proportion of Dublin residents at 21.7%, while the group who became inactive had the
highest proportion of Dublin residents (44.9%) and the lowest proportion of rural dwellers
(41.1%). .
Some 87.3% of the remained active group meanwhile rated their health as good to excellent
compared to 61.2% of the group who remained inactive and 76.1% of those who became
inactive. The group who remained inactive was six years older on average than those who
remained active. Mean age ranged from 63.7 (SD=8.8) for the remained active group to 69.9
(SD=11.3) for those who remained inactive group.
44
Table 3.8 Characteristics of activity change groups
Wave 2
Remained
inactive
%
Became
inactive
%
Became
active
%
Remained
active
%
37.2
26.4
36.4
69.9 (11.3)
47.7
26.5
25.8
66.9 (10.3)
52.5
26.4
21.1
65.5 (10.0)
58.9
27.9
13.1
63.7 (8.8)
34.3
65.7
43.4
56.6
44.2
55.8
55.5
44.5
56.5
11.4
8.0
63.3
10.8
8.4
63.0
13.1
9.4
66.6
13.8
8.6
24.1
17.5
14.4
11.1
Employment status
Retired
Employed
Unemployed
Other
44.7
17.5
4.2
33.6
44.9
27.7
4.3
23.1
41.8
30.9
5.3
22.0
38.1
39.6
5.7
16.6
Location
Dublin city or county
Other town or city
Rural
25.7
29.2
41.1
29.7
29.2
41.1
21.7
32.5
45.8
25.5
31.4
43.1
Highest education
Primary or none
Secondary
Tertiary
39.8
44.0
16.2
35.4
42.5
22.0
29.4
48.3
22.3
26.7
45.9
27.4
Self-rated health
Excellent/v.good/good 61.2
Fair/poor
38.8
76.1
23.9
81.8
18.2
87.3
12.7
Total
100.0
100.0
100.0
Age group
50-64
65-74
75+
Mean age
(sd)
Sex
Male
Female
Marital status
Married
Never married
Separated/
Divorced
Widowed
100.0
N=6871. Missing observations Education 3, Location 8
45
3.3.2 Mental health changes between waves
Turning to average depressive symptoms, these were found to have fallen marginally across
the entire sample with the mean CES-D score down from 6.04 [95%CI: 5.87,6.22] at Wave 1
to 5.59 [95%CI 5.42,5.76] at
Change in depressive symptoms
Figure 3.3 Change in depressive symptoms by
depressive category at baseline
Wave 2. As seen in Figure 3.3
2
there was a high variability in
0
None/mild
-2
Moderate
Severe
Total
this depending on the level of
-4
symptoms at outset as those
-6
suffering “severe” symptoms at
-8
baseline (CES-D≥16) saw the
biggest improvement (M=-8.59 [
-10
95%CI:-9.43,-7.74] ) compared to the group with moderate symptoms (M=-2.59 [95%CI:3.03,-2.16] or the group with no/mild symptoms at outset (CES-D<8) who saw a mean
increase in depressive symptoms (M=1.19, [95%CI:1.06,1.32]). The bigger change seen for
the more depressed groups presumably reflects the fact that they had higher scores in the
first place, meaning they had more scope for variation, but also indicating the need to
include depressive score at baseline into the regression model for predicting change.
The different activity change groups all saw a lessening in depressive symptoms since Wave
1 as shown in Figure 3.4.
Figure 3.4 Mean depression score by activity group at W1 and W2
9
8.2 8.03
CES-D depression score
8
7
6.54
6.26 6.19
6
5.12
5
5.11
4.7
4
3
2
1
0
Remained inactive
Became inactive
W1 depression score
46
Became active
W2 depression score
Remained active
Paired sample t-tests were also carried out for each activity group to compare the
difference between their mean depression scores at Wave 1 and Wave 2. For the ‘Became
active’ group there was a significant difference in the scores at baseline and follow-up (M=1.44, SD=7.18); t(1014)=6.06, p<.001. For the ‘Remained active’ group there was also a
significant difference in the scores (M=-0.45, SD=6.43); t(3543)=4.16, p<.001. However there
was no significant change in depressive symptoms between waves for the group who
remained inactive (M=-0.16, SD=7.90); t(1202)=0.69, p=.489 or the group who became
inactive (M=-0.02, SD=7.10); t(1014)=0.098, p=.922. These suggests that the groups who
became active and remained active saw a real reduction in depressive symptoms between
the two timepoints, but those who remained inactive or became inactive did not experience
a meaningful improvement. The level of change in depressive symptoms for each group is
highlighted in Figure 3.5.
Figure 3.5. Change in depressive symptoms by activity group
Remained inactive
Became inactive
Became active
Remained active
Change in depressive symptoms
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
-1.4
-1.6
Activity change groups
A one-way between-groups analysis of variance was also carried out to explore the
relationship between changes in PA and changes in depressive symptoms and allow for
comparisons between the different activity groups. This again showed that all activity
groups experienced a slight improvement in depressive symptoms between baseline and
follow-up. Welch’s F was used as a more robust alternative to ANOVA. This showed statistically
47
significant differences in average changes in depressive symptoms between the different activity
change groups [F(3, 6675)=7.53, p<.001]. Those who became active saw the biggest improvement
(i.e. reduction) in depressive symptoms followed by those who remained active and those who
remained inactive. However despite reaching statistical significance the difference in changed
depressive symptoms between the groups was very small according to Cohen’s guidelines (1988), as
the effect size calculated using eta squared was 0.004. Post-hoc comparisons using the GamesHowell test showed a statistically significant drop in depressive symptoms for the group who
became active (M= -1.44, SD=7.17) when compared to those who remained inactive (M=-0.16,
SD=7.89), became inactive (M=-0.02, SD=7.09) or who remained active (M=-0.45, SD=6.43), but
there were no significant differences in the changes in depressive symptoms between any of the
latter three groups.
Clinical depression onset
A new dummy variable was derived to capture participants who developed severe
depressive symptoms (CES-D≥ 16) between baseline and follow-up. This showed that
overall 4.6% [95%CI: 4.1, 5.1] of the population developed clinically relevant symptoms of
depression between the two waves.
This was cross-tabulated with the four activity change groups as shown in Table 3.9. It
showed that 7.5% [95%CI:6.9, 8.1] of people who remained inactive and 5.5% [95%CI: 5.0,
6.0] of those who became inactive developed clinically relevant symptoms of depression
between the two waves compared to 3.5% [95%CI: 3.1, 3.9] each for those who became
active and remained active. These differences were found to be statistically significant [χ 2
(3)=38.40, p<.001], but the Cramer’s V measure of effect size was 0.08 which Cohen’s
guidelines (1988) indicate is a small effect. Using the remained inactive group as the
reference, the odds of developing depression was 0.73 lower for the group which became
inactive while those who became or remained active were less than half as likely to develop
depression. This suggests that those who remained or became inactive were more likely to
have become depressed since Wave 1.
48
Table 3.9 Cross-tabulation of physical activity changes and new cases of clinical depression
Newly
depressed
Not
depressed
Odds ratio
Remained
inactive
7.5%
Became
inactive
5.6%
Became
active
3.5%
Remained
active
3.5%
Total %
92.5%
94.4%
96.5%
96.5%
95.4%
0.44
0.44
100.0%
100.0%
1.00
0.73
(reference)
Total
100.0%
100.0%
N=6871. Missing observations 192
4.6%
100.0%
Self rated emotional/mental health changes
A crosstabulation of changes in physical activity and self-rated mental health showed that
the group who became inactive were the most likely to report a worsening of their mental
health since Wave 1 at 36.7%, and least likely to report an improvement at 23.0% This
compares with the group who became active which was most likely to report better mental
health ratings at 28.1% and least likely to report worse mental health at 30.2%. The
remained active group was most stable with 44.7% giving themselves the same mentalhealth rating as at Wave 1 though a below-average proportion rated it better at 23.6%. The
differences between the groups were found to be statistically significant [χ2 (6)=4529.54,
p<.001] but the Cramer’s V measure of effect size was 0.04 which Cohen’s guidelines (1988)
indicate is a very small effect. The fact that a higher proportion of participants rated their
mental health worse rather than better at Wave 2, indicates a discrepancy between this
measure and the depressive symptoms score in which all groups saw a mean improvement
between the two timepoints.
Table 3.10. Crosstabulation of changes in physical activity and self-rated mental health
Change in self-rated
emotional/mental health
No change
Better
Worse
N=6871. Missing observations 3
49
Remained
inactive
Became
inactive
Became
active
Remained
active
Total
40.4%
23.9%
35.8%
40.3%
23.0%
36.7%
41.7%
28.1%
30.2%
44.7%
23.6%
31.7%
42.8%
24.2%
33.0%
3.3.3 Section 2 Summary.
Are older people who increase their level of physical activity more likely to experience
improved mental health than those who remain or become inactive?
Yes. While all activity groups experienced a reduction in depressive symptoms between
waves, those who increased their activity saw the biggest reduction in symptoms, while
those who remained active also saw a significant improvement. By contrast the groups who
remained or became inactive did not experience significant improvements. The group who
became or remained active were also half as likely to have developed clinically significantly
symptoms of depression between the two waves of data collection as those who remained
inactive. They were also more likely to report an improvement in their self-rated mental
health, though this trend was not consistent across all activity groups. However overall the
associations between self-rated mental health and activity were very small.
Are those who reduce their activity level most likely to experience a deterioration in their
mental health?
No. Those who reduced their activity level were more likely to have developed clinically
relevant symptoms of depression at Wave 2 than those who remained or became active but
those who remained inactive were the most likely to have become depressed. However the
group who became inactive experienced a very marginal decrease in depressive symptoms
overall, the smallest such decline seen by any group, and was the most likely to report a
worsening of self-rated mental/emotional health.
50
3.4 Section 3. Changes in mental health controlling for other factors
Do any changes in mental health persist after controlling for other factors which may have
changed over the period such as bereavement, retirement, unemployment, illness and
disability?
While the findings so far indicate an association between changes in physical activity and
changes in mental health, this section will explore whether these can be better explained by
other changes in people’s circumstances over the time period. As outlined in Section 2.5.3
these include new illness and changes in health, changes to people’s physical functioning
capacity, bereavement and/or loss of close relatives/friends, unemployment and
retirement.
3.4.2 Changes in social, economic and health circumstances
The number of people who were widowed since Wave 1 was small at just 1.3%, although
the group who became inactive were twice as likely (1.9%) to have lost their spouse
compared to those who remained active (0.9%) as shown in Table 3.11 below. Just over half
of participants lost at least one close friend or relative over the period, although there was
little variation in this between the activity groups. People who became or remained active
were more likely to have become unemployed since Wave 1 than those who remained
inactive and were also more likely to have retired from the workforce during this period,
although this could reflect the fact that they were younger and more likely to have been in
the workforce at Wave 1.
51
Table3.11. Tabulation of changes in physical activity and social, economic and health changes since Wave 1
Changes since Wave 1
Widowed
Lost 1 or more close
friends or relatives
Became unemployed
Retired
New heart condition/s
New chronic illness
Worse self-rated health
New ADL
New IADL
Worse self-rated health
N=6871
Remained
inactive
1.7%
50.0%
Became
inactive
1.9%
50.8%
Became
active
1.5%
48.6%
Remained
active
0.9%
51.1%
Total
0.8%
2.9%
25.4%
21.8%
26.1%
10.6%
17.5%
26.1%
1.0%
4.5%
21.4%
18.2%
27.0%
5.9%
8.0%
27.0%
1.3%
6.0%
21.2%
16.7%
23.7%
4.1%
5.2%
23.7%
1.6%
5.1%
20.2%
15.9%
23.9%
2.1%
2.5%
23.9%
1.3%
4.7%
21.7%
17.4%
24.8%
4.5%
6.5%
24.8%
1.3%
50.5%
More than one in five people (21.7% [95%CI: 20.7,22.7]) reported a new cardiovascular
condition diagnosed by their doctor since Wave 1, which seems quite a high incidence rate
within two years. This may have been influenced by the fact participants who had a health
assessment at Wave 1 and were found to have high blood pressure or high cholesterol –
often without any previous diagnosis of this - were advised to go to their GP. The new
incidence rate of these conditions was higher than any others at 6.4% [95%CI: 5.8,7.0] and
8.9%[95%CI: 8.2,9.6] respectively. The group who remained inactive had the highest new
incidence rate of cardiovascular conditions at 25.4% [95%CI: 24.4, 26.4] compared to 20.2%
[95%CI: 19.2,21.2]among the group who remained active. The proportion of new chronic
illnesses - a category including conditions such as lung disease, arthrititis, osteoporosis and
cancer - also rose from 15.9% [95%CI: 15.16.8] in the remained active group to
21.8%[95%CI: 20.8,22.8] in the remained inactive groups. Self-reported health meanwhile
worsened for almost a quarter of participants (24.8% [95%CI: 23.8,25.8]) but this was most
pronounced in the group who became inactive at 27.0% [95%CI: 25.9,28.1] compared to
23.7% [95%CI: 22.7,24.7]of those who became active.
There was also a steep gradient between the proportions of people experiencing new
difficulties carrying out everyday tasks, with for example just 2.5% [95%CI: 2.1,2.9]of the
remained active group reporting at least one extra IADL in Wave 2, rising to 17.5% [95%CI:
52
16.6,18.4] of the remained inactive group. A similar pattern, but with a less steep gradient
was recorded for the onset of ADLs.
Model of change in depressive symptoms
In order to assess the relative importance of physical activity compared to other changes in
people’s lives influencing mental health, a hierarchical multiple regression was performed to
predict changes in a person’s depressive symptoms score on the basis of changes in their
physical activity level and other socioeconomic changes since Wave 1.
Diagnostics
The total sample size of 6871 was well above the minimum required for regression analysis.
The unstandardized predicted values were plotted against the studentized deleted residuals
and the resulting plot is shown in Appendix 3. This indicated that the assumption of
homoscedasticity was violated which could increase the risk of a Type 2 error. However as
the values for change in depressive symptoms include negative and positive values it was
not possible to attempt transformation using square root or log. This may weaken the
analysis but does not invalidate it (Tabachnik and Fidell, 2001). The table of standardized
residuals was also inspected and this showed they fell between -4.14 and 7.414 indicating a
number of outliers outside the normal range of -3.29 to 3.29.
A plot of Cook’s distances was inspected to identify influential points that might unduly
influence the final regression equation. One outlier was identified, but its Cook’s value was
0.017 which was well below the threshold of 1 recommended for excluding a case.
Inspection showed that case involved a participant who had become severely depressed by
Wave 2 which was part of the diversity inherent in the sample. Collinearity tests meanwhile
showed that all tolerances were well above 0.1 indicating that collinearity was not a
problem.
3.4.3 Regression results
The first model included bereavement of spouse and/or other close friends/relatives, new
ADLs and IADLs, unemployment, retirement, changes in self-rated health and onset of
cardiovascular conditions or other chronic illnesses. The model showed that these variables
contributed significantly to the regression model [F(9,6668)=17.692, p<.001] and
53
contributed 2.2% of variance. As shown in Table 3.12, the factors with the biggest influence
on change in depressive symptoms since Wave 1 as measured by the standardized
coefficient (β) were having developed a new IADL disability or worse self-rated health,
followed by recent widowhood. Other significant factors were having been diagnosed with a
new cardiovascular condition becoming unemployed or losing close friends or relatives.
Adding gender and age to the model did not explain any further variance in the model and
were not significant so these were excluded from the final model.
For the second model, introducing the CES-D depressive symptoms score at baseline
explained a further 26.7% of variance and was also significant [F(1,6667)=2509.958, p<.001].
When changes in physical activity were then added to the third model this contributed a
very small (0.5%) but statistically significant amount of variance (F(3,6664)=14.877, p<.001].
Altogether this final model accounted for 29.4% of variance in depressive symptoms change
and the biggest factors influencing change were depressive symptoms at baseline (β=0.531), followed by a new IADL disability (0.10), remaining active (-0.8), becoming active (0.7), widowhood (0.07), a new ADL disability (0.05), a new cardiovascular condition (0.05)
worse self-rated health (0.05) and a new chronic illness (0.03). The effect of depressive
symptoms at baseline was increased very slightly when changes in physical activity were
added to the model indicating that those who were more depressed at Wave 1 may have
seen a bigger reduction in symptoms when physical activity was factored in.
These findings indicate that remaining active and becoming active have a significant inverse
relationship to change in depressive symptoms, meaning both contributed to a reduction of
symptoms in this model. Although becoming active had been associated with a bigger drop
in depressive symptoms, the standardized coefficients show that remaining active had a
bigger effect when other life changes were controlled for. As a point of comparison the
effects were similar in magnitude to the effect of spousal bereavement on changes in
depressive symptoms, albeit in a different direction as widowhood led to an increase in
symptoms. Becoming more active and remaining active also had a bigger impact than
developing a new cardiovascular condition a new ADL or a worsening of self-rated health.
The effects of becoming inactive were not found to have a significant impact on the
outcome.
54
Table 3.12 Hierarchical multiple regression model of change in depressive symptoms based on activity changes, depressive
score at baseline and socioeconomic changes
B
β
Model 1
Intercept
Widowed since W1
Lost close friends or
relatives
Retired since Wave 1
Unemployed since W1
New ADL
New IADL
New cardiovascular
condition
New chronic illness
Worse self-rated
health
Model 2
-1.439**
3.649**
0.464*
0.06
0.03
0.659
1.448*
0.605
2.195**
0.617*
0.02
0.02
0.02
0.08
0.04
0.044
1.353**
0.03
0.08
B
Β
Intercept
Widowed since W1
Lost close friends or
relatives
Retired since Wave 1
Unemployed since W1
New ADL
New IADL
New cardiovascular
condition
New chronic illness
Worse self-rated
health
Depressive score W1
Model 3
1.632**
4.612**
0.095
0.08
0.01
-0.388
-0.388
1.856**
3.308**
0.861**
-0.01
0.01
0.06
0.12
0.05
0.511*
0.866**
0.03
0.05
-0.496**
B
-0.525
β
Intercept
Widowed since W1
Lost close friends or
relatives
Retired since Wave 1
Unemployed since W1
New ADL
New IADL
New cardiovascular
condition
New chronic illness
Worse self-rated
health
Depressive symptoms
W1
Became inactive
Became active
2.553**
4.572**
0.087
0.07
0.01
-0.315
0.667
1.729**
2.98**
0.840**
-0.01
0.01
0.05
0.10
0.05
0.477*
0.839**
0.03
0.05
-0.501**
-0.531
-.440
-1.486**
-.02
-.07
Remained active
-1.079**
-.08
*p<.05, **p<.001.
55
R
.153
R2
.023
Adjusted R2
.022
Final model
R=.295
Adjusted R2=.294
R
.539
R2
.290
Adjusted R2
.289
R
.543
R2
.295
Adjusted R2
.294
3.4.4 Likelihood of developing clinical depression
A binary logistic regression was conducted to examine the likelihood of respondents
developing clinically relevant symptoms of depression between the two waves based on
changes in their physical activity level, bereavement of spouse and/or other close
friends/relatives, new ADLs and IADLs, unemployment, retirement, changes in self-rated
health and onset of cardiovascular conditions or other chronic illnesses. The final model was
statistically significant [χ2 (9) = 76.98, p<.001] and the Hosmer and Lemeshow test was nonsignificant suggesting an adequate fit (p=.510). However the overall model explained just
5.2% of variation in whether respondents developed depression or not (Nagelkerke R 2).
The likelihood of respondents developing clinically relevant symptoms of depression was
nearly half as likely for those who became active [OR: 0.557 (95%CI 0.386-0.841), p=.005]
compared to those who remained inactive as shown in Table 3.13. Similarly those who
remained active were 1.7 times less likely to have become depressed [OR: 0.586 (95%CI
0.438-0.785) p<.001]. Respondents who became inactive were marginally less likely to have
developed depression than those who remained inactive but this was not significant [OR:
0.848 (95%CI 0.601-1.198) p=.349].
The other significant factors in predicting likelihood of developing depression were having a
new IADL, a new cardiovascular condition or other chronic illness or having become
unemployed since Wave 1. Widowhood was marginally outside the threshold for
significance, while retirement, developing a new ADL or losing close friends or relatives
were not found to be significant.
Becoming or remaining active were found to be significant factors in predicting the
likelihood of developing depression but becoming inactive was not.
56
Table 3.13 Coefficients of the logistic regression model predicting whether participant developed clinical depressive
symptoms
Constant
Remained inactive
(reference)
Became inactive
Became active
Remained active
Widowed since W1
Lost close friends or
relatives
Retired since Wave 1
Unemployed since W1
New ADL
New IADL
New cardiovascular
condition
New chronic illness
Worse self-rated health
B
SE
95% CI
(lower)
Odds ratio
Exp (B)
0.05
95% CI
(upper)
-3.02**
.148
-0.17
-0.59*
-0.53**
0.70
-0.16
0.18
0.21
0.15
0.37
0.12
0.60
0.39
0.44
0.99
0.68
0.85
0.56
0.59
2.02
0.85
1.20
0.84
0.79
4.13
1.07
0.10
1.11*
0.26
0.93**
0.50**
0.28
0.35
0.22
0.18
0.13
0.64
1.52
0.84
1.78
1.28
1.10
3.02
1.30
2.53
1.64
1.91
6.00
2.00
3.59
2.10
0.28*
0.21
0.14
0.13
1.00
0.96
1.32
1.23
1.73
1.59
*p<.05, **p<.001. R2= 0.052( Nagelkerke), 0.016 (Cox and Snell). Model χ2(12)=112.34, p<.001.
57
Section 3 Summary
Do any changes in mental health associated with changes in physical activity
persist after controlling for other factors which may have changed over the period
such as bereavement, retirement, unemployment, new illness and disability?
Yes. Becoming or remaining active contributed significantly to a reduction in
depressive symptoms even when other social, economic and health changes were
controlled for. This was found to be the case both for overall symptoms and for the
likelihood of developing clinically relevant symptoms of depression. The impact of
becoming or remaining active on depressive symptoms was similar in magnitude
(though opposite in direction) to widowhood.
However the impact of becoming inactive was not found to contribute significantly
to changes in depressive symptoms or the likelihood of developing clinical
depression.
3.5 Recap
The findings outlined in this chapter show
- that people doing more physical activity experience better mental health than less active
groups
- that those who increase their level of physical activity are more likely to see an
improvement in mental health and are half as likely to develop depression as those who
remain inactive
- that reducing activity level is not associated with a deterioration in mental health
compared to remaining inactive
- that the changes observed persist even when controlling for other changes over the same
period.
These findings and their relation to previous studies and implications for further research
will be discussed in the next chapter.
58
Chapter 4: Discussion
4.1 Introduction
This study sought to explore the relationship between changes in physical activity and
changes in mental health in later life by examining social, economic and health differences
between participants with different activity levels, by seeing if changes in activity in later life
was associated with changes in depressive symptoms, and to see if any association found
would persist even with controlling for other factors that may affect mental health such as
new chronic illness, disability or unemployment.
It found that the more active groups tended to be younger and participants were more
likely to be male, better-educated, married, working and enjoy better health than less active
groups, while also reporting fewer depressive symptoms. They were also less likely to suffer
from or develop clinically relevant symptoms of depression. However when it came to
changes in activity levels the group who became more active had higher proportions of
women and rural dwellers.
It also found that while all groups experienced a reduction in depressive symptoms over the
period of the study, this was only significant for those who increased their activity or
remained active. Those who reduced or maintained a low level of activity were more likely
to have developed clinically relevant symptoms of depression and worse self-rated
emotional/mental health.
The association between increased physical activity and improved mental health persisted
even when controlling for the social, health and economic changes outlined. Becoming or
remaining active was significantly, and inversely related to a change in depressive
symptoms. However it still only accounted for a very small proportion (0.5%) of variance in
the changes in depressive symptoms. However the converse hypothesis that reducing
physical activity would lead to an increase in depressive symptoms was not found to be
supported by the evidence.
4.2 Characteristics associated with activity change
59
The group who became active was younger, better-educated and more likely to be working
and enjoying better health than those who remained or became inactive. However a similar
profile was not found in a Japanese study which found those who became or remained
active were slightly older and less highly-educated than those who remained sedentary
although that was a much smaller sample of 680 people with an older age profile at baseline
(Yoshida et al, 2015). Both studies found that a majority of those who became active were
female, which in the present study was surprising given the general predominance of men in
the more active groups at each individual wave. The finding that those who became more
active later in life were more likely to be rural dwellers and less likely to live in Dublin
whereas an opposite trend was seen amongst those who became inactive is noteworthy and
contrasts with a previous Irish study which found that living in a rural area is significantly
associated with physical inactivity amongst women (Murtagh et al, 2015).
The markedly higher self-ratings for health and the lower incidence of new diagnoses of
cardiovascular and other chronic conditions of the newly active and remained active groups
in the present study mirror those of a UK study that found these groups were much more
likely to have aged healthily and be free of chronic disease, cognitive impairment and
functional limitations (Hamer et al, 2016).
4.3 Changes in physical activity and mental health
This study found a significant reduction in depressive symptoms for those who increased
their physical activity level and a slightly smaller but still significant improvement for those
who maintained a moderate/high level of activity. When adjusted in a regression model to
take account of other changes in people’s circumstances since Wave 1 such as widowhood,
illness, disability and economic transitions, the largest association was seen for the group
who remained active. However becoming active remained significantly inversely related to
changes in depressive symptoms and the effect was similar in magnitude to widowhood.
The groups who became or remained active were also nearly half as likely to develop
clinically relevant symptoms of depression as those who remained inactive.
These findings correspond with many previous reports finding a significant but relatively
small association between changes in physical activity and mental health. Several meta60
analyses (Cooney et al, 2013; Rebar et al, 2015) found evidence of a small to moderate
effect of exercise/physical activity though these involved randomised control trials rather
than prospective analysis. Some other meta-analyses have reported a larger effect
(Josefsson et al, 2014) than was seen in this study. The only meta-analysis specifically
examining the impact of exercise trials on depression in older adults (Heinzel et al, 2015)
also found a moderate effect.
Turning to other longitudinal studies which are more directly comparable, this study’s
findings are very similar to those of Yoshida et al (2015), which found that older adults who
remained active over three years were half as likely to experience the onset of depression as
the inactive group after adjusting for confounding variables. However that Japanese study
found a non-significant reduction in the likelihood of depression for the ‘newly active’ group
which contrasted with the present study’s finding of an identical (and significant) reduction
in odds for both the newly active and remained active groups. Likewise, the present study
mirrors the findings from research using the US Health and Retirement Study of a significant
depression-buffering effect of physical activity for those who continued or began exercise:
“Although the protective effect of exercise appears to be small, the significance itself
after controlling for the other risk factors shows that physical exercise is likely to be
a valuable preventive intervention (Choi and Bohman, 2007:173-)
However the current study differed from an Israeli study which found that when health
measures were added, the correlations between commenced activity and depression
disappeared while the correlation between remaining active and depression was reduced.
4.4 Activity changes
There was a markedly high level of transition between activity levels amongst the study
population with, for example 42.6% of participants who reported low activity at Wave 1
transitioning to moderate or higher activity at Wave 2. This is an important issue to look at
as it is central to defining the subgroups used for this study. The changes cannot be
accounted for by a change in measurement as the same short-form IPAQ was used in the
CAPI questionnaire at both waves. Limitations of the IPAQ have been noted including its
relatively low thresholds for ‘moderate’ activity, challenges in recall and the fact it was
specifically designed for use in adults aged 18-69 but is often used in both older and
61
younger cohorts (Bauman et al, 2009). However that study also noted IPAQ findings did
appear to be cross-sectionally related to obesity and other health risks, and longitudinally to
cardiovascular event rates (Bauman et al. 2009). It is also possible that social desirability
could have motivated some participants to overstate their level of physical activity as has
been noted in previous research (Motl, McAuley and DiStefano, 2005).
4.4.1 Timing as possible factor
The timing of interviews was another factor which may have influenced the high activity
transition rates. Whereas the timing of Wave 1 interviews was split very evenly between
winter and summer, the large majority (84.0%) of Wave 2 interviews were held in the
summer. Previous literature has indicated that physical activity is higher during summer
months (Berger, Mutrie, Hannah and Der, 2000) and this could help explain the sizable
cohort increasing their activity levels by Wave 2. However against this, an even larger group
also transitioned from high/medium activity to low activity despite experiencing a similar
seasonal shift in timing of interviews. It is also possible that seasonality played a role in the
surprising finding that depressive symptoms fell slightly for all groups, and that the overall
proportions experiencing clinically relevant symptoms declined slightly between waves.
Previous longitudinal research in the non-clinical population has indicated strong and highly
significant seasonal effects in depressive symptoms amongst the non-clinical population
(Harmatz, et al, 2000), while seasonal affective disorder is an established condition,
although with a relatively low frequency (Partonen and Lonnqvist, 1998).
Social relevance
Depression imposes a major burden on individuals and the state in terms of personal
suffering, lost productivity and healthcare costs, so cost-effective measures that can help
alleviate or prevent it are important. While the link between physical activity and better
physical health appears unimpeachable the evidence of a link between activity and mental
health has been more contentious. The association found in this study provides evidence of
the benefits to mental health associated with increased activity in later life which, from a
public health perspective, could provide a useful message that might have more short-term
appeal in motivating behaviour change than the longer-term physical health gains. The
finding that more women became active between waves is also an encouraging one given
62
women’s generally lower levels of activity and higher rates of depression, and suggest this
group could be receptive to public health campaigns aimed at encouraging higher activity in
the older population. The findings that Dublin dwellers are more likely to become inactive
and less likely to increase activity levels could also be considered in finding ways to promote
higher activity levels.
While an association between changes in physical activity and mental health has been
shown, the issue of causality cannot be addressed in this study because it is impossible to
know from the data whether any change in physical activity predated and possibly
influenced a change in depressive symptoms or whether changes in mental health predated
and influenced changes in physical activity patterns. Further waves of data along with
objective measurements of physical activity may help to establish causality identifying
longer-term changes and outcome trends.
4.6 Evaluation of study
This study benefitted from a large well-designed longitudinal survey of older people which
ensured there were adequate numbers and statistical power to carry out analyses of
different subgroups on a range of different variables and with the use of attrition weighting
to adjust for loss of participants and original sample bias.
The identification of four distinct activity change subgroups allowed for the analysis of
comparative changes in mental health over the two-year period, controlling for a range of
socioeconomic and health changes that could also influence mental health.
4.6.1 Limitations
Two waves of longitudinal data can only begin to identify trends in participants’ activity
patterns and mental health and the measures used involving seven-day recall cannot
establish whether these changes are mere fluctuations or involve more persistent change.
Unlike a randomized control trial, this study could not establish causality in the relationship
between changes in PA and mental health as better mental health could lead to higher
activity or vice versa, or there could be other explanatory factors such as increased social
engagement which leads to improvements in both.
63
As discussed a question mark also remains about the reliability of the IPAQ data in tracking
changes in physical activity. It is hoped that a more objective measure of physical activity
which could also provide cross-validation for IPAQ as a measure will transpire from the use
of accelerometers to measure participants’ activity patterns in Wave 3 of TILDA (Nolan et al,
2013).
An income change variable could not be derived for the present study because of complex
differences in measurement of income between the two waves. Its inclusion in future
analysis would be helpful as a further control variable.
4.7 Further research
Given the importance of physical activity to physical health, the study methodology could be
extended to look at health outcomes for the different activity change groups identified,
particularly when objective Wave 3 health assessment data becomes available for analysis.
A longer timeframe would also be helpful to assess the impact of changes in physical activity
and longer-term depression scores to see if the differences identified persist, increase or
disappear. The inclusion of accelerometer data from Wave 3 of TILDA would be highly
beneficial to validate the findings (or not) and establish if reported levels of physical activity
correspond with reality and to examine dose-response relationship between activity levels
and mental health. Further analysis of the timing of interviews as an influence on outcomes
would also be of interest and other socioeconomic changes could be included. Inclusion of
attrition weights in the public anonymised dataset of TILDA is highly recommended to
facilitate wider use of longitudinal analysis of the dataset.
4.8 Conclusion
Increasing and maintaining physical activity was associated with a reduction in depressive
symptoms that was similar in magnitude (though not in direction) to the impact of recent
widowhood. Both were also associated with a significantly reduced likelihood of developing
depression. The overall contribution of physical activity changes to variance in depressive
symptoms was small but significant.
64
Bibliography
Allum N & Arber S. 2008. ‘Secondary Analysis of Survey Data’ in N. Gilbert (ed.) Researching
Social Life. Sage.
American Psychological Association n.d. Centre for Epidemiological Studies-Depression.
http://www.apa.org/pi/about/publications/caregivers/practicesettings/assessment/tools/depression-scale.aspx. Retrieved 26th July 2016.
Barrett A, Savva G, Timonen V, Kenny R. Fifty Plus in Ireland 2011: First results from the Irish
Longitudinal Study on Ageing (TILDA). Dublin: The Irish Longitudinal Study on Ageing.
Berger U, Mutrie N, Hannah M, Der G. 2000. A Longitudinal Analysis of Physical Activity
Patterns of Adults within their early 60s. Medical Research Council Social & Public Health
Sciences Unit Occasional Paper No 11. Glasgow.
Biddle S. and Mutrie N. 2008. Psychology of Physical Activity. Determinants, well-being and
interventions (2nd). Oxon. Routledge.
Birkeland,M, Torsheim T, Wold B. 2009. ‘A longitudinal study of the relationship between
leisure-time physical activity and depressed mood among adolescents. Psychology of Sport
and Exercise 10 (2009) 25-34.
Blumenthal J, Babyak M, Doraiswamy P, Watkins L, Hoffman B, Barbour K, Herman S,
Craighead W, Brosse A, Waugh R, Hinderliter A, Sherwood A. ‘Exercise and
pharmacotherapy in the treatment of major depressive disorder’. Psychosomatic Medicine.
69:587–59
Bryman A. 2016. Social Research Methods. Oxford. Oxford University Press.
Central Statistics Office, 2004. ‘Population and Labour Force Projections. 2006-2036’.
Stationery Office Dublin.
Chisholm D, Sweeny K, Sheehan P, Rasmussen, B, Smit F, Cuijpers P, Sakhena S. 'Scaling
up,treatment of depression and anxiety: a global return on investment analysis'. Lancet
Psychiatry 2016: 3: 415-24.
Choi N, Bohman T, 2007. ‘Predicting the Changes in Depressive Symptomatology in Later
Life. How Much Do Changes in Health Status, Marital and Caregiving Status, Work and
Volunteering, and Health-Related Behaviors Contribute?’ Journal of Aging and Health: 19:1:
52-177
Cohen J. 1988, Statistical power and analysis for the behavioral sciences (2nd ed.), Hillsdale,
New Jersey, Lawrence Erlbaum Associates, Inc.
Cole M, Dendukuri N. 2003. ‘Risk Factors for Depression Among Elderly Community
Subjects: A Systematic Review and Meta-Analysis.’ The American Journal of Psychiatry.
160:6: 1147-1156.
65
Conn V. 2010 ‘Depressive symptom outcomes of physical activity interventions: Metaanalysis findings’ Annals of Behavioural Medicine: 39:2: 128-138.
Cooney G, Dwan K, Greig C, Lawlor D, Rimer J, Waugh F, McMurdo M, Mead G, 2013.
‘Exercise for depression (Review)’ Cochrane Database of Systematic Reviews: 9:CD004366
Cooney G, Dwan K, Mead G, 2014. ‘Exercise for Depression’. Journal of the American
Medical Association 311:23: 2432-2433.
Craft L. 2011. ‘Exercise and clinical depression: examining two psychological mechanisms.’
Psychology of Sport and Exercise.’ 6:2: 151-171.
Craig, C, Marshall, A, Sjostrom, M, Bauman, A, Booth, M, Ainsworth, B. Pratt, M, Ekelund, U.
Yngve, A., Sallis, J, & Oja, P, 2003. ‘International physical activity questionnaire: 12-country
reliability and validity’. Med Sci Sports Exerc, 35, 1381–95. 17.
Crombie I, Irvine L, Williams B, McGinnis A, Slane P, Alder E, McMurdo M, 2004. ‘Why older
people do not participate in leisure time physical activity: a survey of activity levels, beliefs
and deterrents’ in Age and Ageing 33:3. 287-292 (HC)
Cronin H, O’Regan C, Finucane C, Kearney P and Kenny RA, 2013. ‘Health and Aging:
Development of The Irish Longitudinal Study on Ageing Health Assessment’. Journal of the
American Geriatrics Society 61:S269-S278.
Department of Health and Children (DoH). 2009. National Guidelines on Physical Activity for
Ireland. www.health.gov.ie Retrieved 25th March 2016.
Department of Health, 2016. ‘Get Ireland Active. National Physical Activity Plan for Ireland’.
http://health.gov.ie/wp-content/uploads/2016/01/Get-Ireland-Active-the-NationalPhysical-Activity-Plan.pdf. Retrieved 20th April 2016.
De Vaus, D. 2001. Research Design in Social Research. London: Sage
De Vaus, D. 2001 Surveys in Social Research. London: Sage/
Donoghue O, O’Connell M, Kenny RA. 2016. ‘Walking to Wellbeing: Physical Activity, Social
Participation and Psychological Health in Irish adults aged 50 years and Older. The Irish
Longitudinal Study on Ageing (TILDA), Trinity College Dublin.
Dooley C. 2014. ‘Methodology’ in The Over 50s in a Changing Ireland. Economic
Circumstances, Health and Well-Being.
Doyle F, Conroy R, Hickey A, Kelleher C, 2014. ‘Pain, physical activity and depressive
symptoms in older people.’ Research Brief. Centre for Ageing Research and Development in
Ireland. Dublin.
Ekkekakis P, Hall E, Petruzzello S, 2005. ‘Variation and homogeneity in affective responses to
physical activity of varying intensities: an alternative perspective on dose-response based on
evolutionary considerations.’ Journal of Sports Sciences: 23: 477-500.
66
Ekkekakis P, Parfitt G, Petruzzello S. 2011. ‘The Pleasure and Displeasure People Feel When
they Exercise at Different Intensities: Decennial Update and Progress towards a Tripartite
Rationale for Exercise Intensity Prescription.’ Sports Medicine: 41:8: 641-671.
Ekkekais P, 2015 'Honey, I shrunk the pooled SMD! Guide to critical appraisal of systematic
reviews and meta-analyses using the Cochrane review on exercise for depression as
example.' Mental Health and Physical Activity: 8:21-36
Elavsky S, McAuley E, Motl R and Konopack, 2005. ‘Physical Activity Enhances Long-Term
Quality of Life in Older Adults: Efficacy, Esteem and Affective Influences’. Annals of
Behavioural Medicine: 30:2: 138-145.
Finucane C, Feeney J, Nolan N, O’Regan C. 2014. ‘Changes in Physical and Behavioural
Health in Older Irish Adults’ in Nolan et al. The Over 50s in a Changing Ireland: Economic
Circumstances, Health and Well-Being. Dublin: The Irish Longitudinal Study on Ageing
Fox K, 1999. ‘The influence of physical activity on mental well-being’. Public Health Nutrition
2(3a):411-418
Hamer M, Lavoie K, Bacon S. 2014. ‘Taking up physical activity in later life and healthy
ageing: the English longitudinal study of ageing’. British Journal of Sports Medicine. 48: 239243.
Harmatz M Well A, Overtree C, Kawamura K, Rosal M and Ockene I. 2000. ‘Seasonal
Variation of Depression and Other Moods: A Longitudinal Approach.’ Journal of Biological
Rhythms: 15:4: 344-350.
Heinzel S, Lawrence J, Kallies G, Rapp M, Heissel A, 2015. ‘Using exercise to fight depression
in older adults: A systematic review and meta-analysis’. The Journal of Gerontopsychology
and Geriatric Psychiatry: 28:4:149-162.
Hidaka, B. 2012. ‘Depression as a disease of modernity: explanations for increasing
prevalence’. Journal of Affective Disorders: 140:3:205-214.
IPAQ. 2004. ‘Guidelines for data processing and analysis of the International Physical Activity
Questionnaire (IPAQ) – Short Form’.
http://www.institutferran.org/documentos/scoring_short_ipaq_april04.pdf. Retrieved 4th
June 2016.
Jak, AJ, 2012. ‘The impact of physical and mental activity on cognitive aging’. Curr Top
Behavioural Neuroscience 10:273-91.
Josefsson T, Lindwall M, Archer T. 2014. ‘Physical exercise intervention in depressive
disorders: Meta-analysis and systematic review.’ Scandinavian Journal of Medicine & Science
in Sports. 24:2: 259-272.
Kenny, R. A, Whelan, B, Cronin, H, Kamiya, Y, Kearney, P, O’Regan, C. & Ziegel, M. 2010. The
design of the Irish longitudinal study on ageing. Ireland: Trinity College Dublin.
67
Kessler R, Berglund P, Demler O, Jin R, Merikangas K, Walters E. 2005. ‘Lifetime prevalence
and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey
replication’. Archives of General Psychiatry: 62:593–602.
Khalaila R, Litwin H. 2014. ‘Changes in health behaviors and their associations with
depressive symptoms among Israelis aged 50+’. Journal of Aging and Health: 26:3: 401-421.
Khatta M, 2008. Theoretical Models and Interventions to Increase Physical Activity Among
Adults: A Historical Review. Topics in Advanced Practice Nursing eJournal. 2008;8 (1). (HC)
Kop W, Weinstein A, Deuster K, Whittaker R, Tracy. 2008. ‘Inflammatory markers and
negative mood symptoms following exercise withdrawal’ Brain Behav Immun 22: 11901196.
Kvam S, Kleppe C, Nordhus I, Hovland A. 2016. ‘Exercise as a treatment for depression: A
meta-analysis’. Journal of Affective Disorders: May 20: 205-67-86 [Epub ahead of print]
Layte R, Sexton E, Savva G. 2013. Quality of Life in Older Age: Evidence from an Irish Cohort
Study. 2013. Journal of the American Geriatrics Society. 61(s2): S299-305.
Lewinsohn P, Seeley J, Roberts R, Allen N. 1997. ‘Centre for Epidemiologic Studies
Depression Scale (CES-D) as a screening instrument for depression among communityresiding older adults’. Psychology and Aging 12:2: 277-287.
Lindwall M, Larsman, P, Hagger, M. 2011. ‘The reciprocal relationship between physical
activity and depression in older European adults: A prospective cross-lagged panel design
using SHARE data.’ Health Psychology: 30: 4: 453-462
Lopresti A, Hood S, Drummond P. 2013. ‘A review of lifestyle factors that contribute to
important pathways associated with major depression: diet, sleep and exercise’. Journal of
Affective Disorders 148: 12-27.
Mammen G, Faulkner G, 2013. ‘Physical Activity and the Prevention of Depression: A
Systematic Review of Prospective Studies’. American Journal of Preventive Medicine.45:5:
649-657.
Mawani F and Gilmour H. 2010. ‘Validation of self-rated mental health’. Health Reports: 21:
3: 61-75.
McKee, G., Kearney, P., Kenny, R. 2015. The factors associated with self-reported physical
activity in older adults living in the community. Age and Ageing. 44(4):586-92.
Measurement Instrument Database for the Social Sciences (MIDSS)
http://www.midss.org/content/center-epidemiologic-studies-depression-scale-ces-d.
Retrieved 26th July 2016.
Mnookin, S. 2016. ‘Out of the Shadows. Making Mental Health a Global Development
Priority’. World Bank Group and World Health Organization. 2016.
Motl R, McAuley E and DiStefano C. 2005. ‘Is social desirability associated with self-reported
physical activity?’ Preventive Medicine. 40:6: 735-739.
68
Murphy C, Kearney P, Shelley E, Fahey T, Dooley C and Kenny R. 2015. ‘Hypertension
prevalence, awareness, treatment and control in the over 50s in Ireland: evidence from The
Irish Longitudinal Study on Ageing.’ Journal of Public Health: doi; 10.1093/pubmed/fdv057.
Murtagh EM, Murphy MH, Murphy NM, Woods C, Nevill AM, Lane A. 2015. ‘Prevalence and
Correlates of Physical Inactivity in Community-Dwelling Older Adults in Ireland’. PLoS ONE
10(2): e0118293.
Newsom J, Jones R and Hofer, S. 2012. Longitudinal Data Analysis. A Practical Guide for
Researchers in Aging, Health and Social Sciences. Routledge. New York.
Nolan A, O’Regan C, Dooley C, Wallace D, Hever A, Cronin H, Hudson E, Kenny R. 2014. ‘The
Over 50s in a Changing Ireland: Economic Circumstances, Health and Well-Being’. Dublin:
The Irish Longitudinal Study on Ageing.
O’Cionnaith, F. 2016. Varadkar accused of raiding this year’s budget for mental health. Irish
Examiner 22nd April 2016.
http://www.irishexaminer.com/ireland/varadkar-accused-of-raiding-this-years-budget-formental-health-394342.html Retrieved 25h April 2016.
O’Shea E and Kennelly B. 2008. ‘The Human Cost. An overview of the evidence on economic
adversity and mental health and recommendations for action’. Mental Health Commission.
Dublin.
Osvaldo P, Khan K, Hankey G, Yeap B, Golledge J, Flicker L. 2014. ‘150 minutes of vigorous
physical activity per week predicts survival and successful ageing: a population-based 11year longitudinal study of 12,201 older Australian men’ in Br J Sports Med 2014: 48:220-225
Partonen T and Lonnqvist J. 1998. ‘Seasonal affective disorder’. The Lancet:352:9137: 13691374.
Penedo, Frank Ja; Dahn, Jason R 2005. ‘Exercise and well-being: a review of mental and
physical health benefits associated with physical activity’. Current Opinion in Psychiatry
Vol.18: 2: 189-193.
Rebar A, Stanton R, Geard D, Short C, Duncan M, Vandelanotte C, 2015. ‘A meta-metaanalysis of the effect of physical activity on depression and anxiety in non-clinical adult
populations’. Health Psychology Review 9: 3: 366-378. (HC)
Radloff, L.S. (1977) ‘The CES-D scale: A self-report depression scale for research in the
general population’. Applied Psychological Measurement 1: 385-401.
Rethorst C, Wipfli B, Landers D, 2009. ‘The Antidepressive Effects of Exercise’ A MetaAnalysis of Randomized Trials’. Sports Medicine 39: 6: 491-511
Roberts R, Vernon S, Rhoades H, 1989. ‘Effects of Language and Ethnic Status on Reliability
and Validity of the Center for Epidemiologic Studies-Depression Scale with Psychiatric
Patients’. Journal of Nervous & Mental Disease. 1077:10: 581-653.
69
Rodda, J. 2011. ‘Depression in older adults. Clinical Review.’ BMJ 2011: 343: d5219.
Schuch F, Vancampfort D, Richards J, Rosenbaum S, Ward, P, Stubbs B, 2016. ‘Exercise as a
treatment for depression: A meta-analysis adjusting for publication bias’. Journal of
Psychiatric Research: 77. June 2016: 42-51.
Smits J, Berry A, Rosenfield D, Powers M, Behar E, Otto M. 2008. ‘Reducing anxiety
sensitivity with exercise.’ Depression and Anxiety: 25:8: 689-699.
Stathopoulou G, Powers M, Berry A, Smits J, Otto M. 2006. ‘Exercise Interventions for
Mental Health: A Quantitative and Qualitative Review.’ Clinical Psychology: Science and
Practice: 13:2: 179-193.
Strawbridge W, Deleger S, Roberts R, Kaplan G. 2002. Physical Activity Reduces the Risk of
Subsequent Depression for Older Adults. American Journal of Epidemiology: 156:4: 328-334
Sun F, Norman I, While A. 2013. ‘Physical activity in older people: a systematic review’. BMC
Public Health 10.1186: 1471-2458.
Tabachnik B and Fidell L, 2001. Using Multivariate Statistics. 4th Ed. Allyn and Bacon.
Tiedt A, 2013.’Cross-National Comparisons of Gender Differences in Late-Life Depressive
Symptoms in Japan and the United States’. Journals of Gerontology, Series B: Psychological
Sciences and Social Sciences: 68:3: 443-454.
The Irish Longitudinal Study on Aging 2014 (no author) ‘Weighing’. Internal TILDA document.
TILDA Weights Codebook Wave 1 to Wave 3. July 2013. Retrieved from TILDA research
database 28th July 2016.
United Nations. 2015. https://sustainabledevelopment.un.org/?menu=1300 retrieved 1st
July 2016
Weir, K. 2011 ‘The Exercise Effect’ in Monitor on Psychology. American Psychology
Association. Retrieved from http//www.apa.org/monitor/2011/12/exercise.aspx 30 th March
2016.
Wen CP, Wai JP, Tsai MK, Yang YC, Cheng TY, Lee MC, Chan HT, Tsao CK, Tsai SP, Wu X:
Minimum amount of physical activity for reduced mortality and extended life expectancy: a
prospective cohort study. Lancet. 2011, 378: 1244-1253.
WHO, 2006. Physical Activity and Health in Europe: Evidence for Action. Geneva, World
Health Organization (2006). http://www.euro.who.int/document/e89490.pdf
WHO, 2010. Global Recommendations on Physical Activity for Health. www.who.int
Yoshida Y, Iwasa H, Kumagai S, Suzuki T, Awata S &Yoshida H, 2015. ‘Longitudinal
assosciation between habitual physical activity and depressive symptoms in older people’.
Psychiatry and Clinical Neurosciences: 69:686-692.
70
Appendices
Appendix 1
IPAQ short form questionnaire. From The Irish Longitudinal Study on Ageing Wave 2 CAPI
Questionnaire. 30-10-2013. Dublin. Retrieved from www.ucd.ie/issda/data/tilda/ 25th
August 2016.
10.2 Exercise section
INTRO: The next set of questions will ask you about the time [you/Rname] spent being
physically active in the last 7 days.
Vigorous physical activities refer to activities that take hard physical effort and make
[you/him/her] breathe much harder than normal. Think only about those physical activities
that [you/he/she] did for at least 10 minutes at a time.
BH101: During the last 7 days, on how many days did [you/he/she] do vigorous physical
activities like heavy lifting, digging, aerobics, or fast bicycling?
1. _____Number of days per week
0. No [I/he/she] [have/has] not done any vigorous physical activities
GO
TO
BH103
98. DK/ NOT SURE
99. RF
BH102: How much time did [you/he/she] usually spend doing vigorous physical activities on
one of those days?
_____ hours per day (0 …10)
_____ minutes per day
[bh102a]
98. DK/NOT SURE
99. RF
BH103: Moderate activities refer to activities that take moderate physical effort and make
[you/him/her] breathe somewhat harder than normal. Think only about those physical
activities that [you/he/she] did for at least 10 minutes at a time. During the last 7 days, on
how many days did [you/he/she] do moderate physical activities like carrying light loads,
bicycling at a regular pace, or doubles tennis? Do not include walking.
1. _____ days per week
0. No [I/he/she] [have/has] not done any moderate physical activities
BH105
98. DK
99. RF
GO
TO
BH104: How much time did [you/he/she] usually spend doing moderate physical activities on
one of those days?
_____ hours per day (0 …10)
71
_____ minutes per day
98. DK/NOT SURE
99. RF
[bh104a]
BH105: Now think about the time [you/he/she] spent walking in the last 7 days. This includes
at work and at home, walking to travel from place to place, and any other walking that
[you/he/she] might do solely for recreation, sport, exercise, or leisure.
During the last 7 days, on how many days did [you/he/she] walk for at least 10 minutes at a
time?
1. _____ days per week
0. No [I/he/she] [have/has] not done any walking
GO TO BH107
98. DK
99. RF
BH106: How much time did [you/he/she] usually spend walking on one of those days?
_____ hours per day (0 …15)
_____ minutes per day
[bh106a]
98. DK/NOT SURE
99. RF
Appendix 2. Centre for Epidemiologic Studies Depression (CES-D) Scale questionnaire
Centre for Epidemiologic Studies Depression Scale. From The Irish Longitudinal Study on
Ageing Wave 2 CAPI Questionnaire. 30-10-2013. Dublin. Retrieved from
www.ucd.ie/issda/data/tilda/ 25th August 2016.
Mental health
Depression
IWER: SHOW CARD MH1
*IF (HH005 = 2,3,4,5 OR 6 - PROXY INTERVIEW) GO TO MH022
INTRO: The next section of the interview is about people’s mood, feelings and well-being. I
am going to read a list of statements that describe some of the ways you may have felt or
behaved in the last week. Please look at this card and indicate how often you have felt this
way during the past week.
MH001: I was bothered by things that usually don't bother me
72
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
1.
Rarely or none of the time (less than 1 day)
2.
Some or a little of the time (1-2 days)
3.
Occasionally or a moderate amount of time (3-4 days)
4.
All of the time (5-7 days) 98.
DK 99.
RF
Response categories are the same as above for each question that follows.
MH002: I did not feel like eating; my appetite was poor.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH003: I felt that I could not shake off the blues even with help from my family or friends.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH004: I felt that I was just as good as other people.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH005: I had trouble keeping my mind on what I was doing.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH006: I felt depressed.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH007: I felt that everything I did was an effort.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH008: I felt hopeful about the future.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH009: I thought my life had been a failure.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH010: I felt fearful.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH011: My sleep was restless.
73
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH012: I was happy.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH013: I talked less than usual.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH014: I felt lonely.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH015: People were unfriendly.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH016: I enjoyed life.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…?'
MH017: I had crying spells.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…?'
MH018: I felt sad.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH019: I felt that people disliked me.
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
MH020: I could not get "going."
IWER: PROMPT IF NECESSARY - 'WOULD YOU SAY THIS STATEMENT DESCRIBES THE WAY
YOU FELT DURING THE PAST WEEK RARELY…….. SOME OF THE TIME…..?'
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Appendix 3. Residual Plot of regression model for depressive symptoms change
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Appendix 4. Cooks distance plot of final regression model of depressive symptoms
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