Anxiety and Cognitive Function Relationship

This assignment delves into the intricate connection between anxiety and cognitive performance. Students are tasked with analyzing research papers that examine how anxiety impacts various cognitive functions such as memory, attention, and executive functioning. The analysis should focus on identifying key findings, exploring the underlying mechanisms, and discussing the implications of this relationship for individuals experiencing anxiety.

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CHAPTER TWO

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ANXIETY, DEPRESSION, COGNITIVE PERFORMANCE, AND COGNITIVE DECLINE IN
YOUNG AND ELDERLY ADULTS

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ABSTRACT
INTRODUCTION: The current study aims at examining the relationship between anxiety, depression, cognitive function and its decline among
elder adults and younger people.
METHODS: Two set of participants including 52 undergraduates from the psychology department at Swansea University, with 31 females and
21 males and 52 elder people from the general public between the ages of 50 and 80, 32 females and 20 males had been selected.
RESULTS: The study found significant difference in the anxiety level among elderly adults and youngsters at sig. value below 0.05. However,
gender did not showcase significant difference. Besides this, the result found that anxiety and depression are favorably associated with each
other. There is correlation between anxiety score and objective function MOCA as it can be observed that value of correlation is 0.77 which
means that there is high relationship between both variables. It can be said that both variables are correlated to each other.
CONCLUSION: The study results concluded that sig. difference exists in the anxiety and cognitive score among both the elderly people and
youngsters. It is also concluded that BAI, state anxiety, trait anxiety and BDI have significant difference in respect to both the younger and
elderly adults. It is also identified that in comparison to older aged people youngsters are heavily are heavily affected by anxiety. In case of male
it is observed that age have negative relationship with anxiety in case of male but same correlation is observed positive in case of female.
Keywords: Anxiety, Depression, Cognitive decline, Objective cognitive function, Subjective memory function, Ageing

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INTRODUCTION
This study investigated the relationship between anxiety, depression, cognitive function and cognitive decline in both older and younger
adults. Prior research implies that precursors of dementia include mild cognitive impairment, and precursors of mild cognitive impairment
include subjective cognitive impairment. Factors associated with the development of dementia have included such elements as general health,
diabetes, cardiovascular issues, and psychological problems. Understanding the causes of dementia is thus a challenging task.
This investigation focused on several factors. These factors included anxiety, depressive symptoms, subjective memory function,
objective cognitive function, as well as demographic information. This chapter presents a brief review of previous literature before describing
the methodology and individual instruments used in this investigation. The literature review first presents an overview of neurophysiological
theories of cognitive functions to serve as an underpinning for this research. It critically examines subjective cognitive impairment, objective
cognitive impairment and moderate impairment. Mild cognitive impairment (MCI) refers to expected cognitive decrease in normal aging and
more serious decline of dementia. It includes thinking difficulty, language issue, memory and judgmental problems that are greater in
comparison to normal age-related changes. Thus, the aim of the literature review is to critically evaluating anxiety, depression, cognitive
performance and cognitive decline in young and elder adults. The brief literature review identifies gaps in the current literature providing a
justification for the conduct of the current study. The literature review is then followed by an in-depth discussion of the methodology used in this
study, followed by a presentation of the research results and a discussion of the implications of those results, along with an explanation of the
limitations of the current study and suggestions for further research.
LITERATURE REVIEW
Neuropsychological assessment has become more and more important in healthcare practices and is commonly used for diagnosis,
treatment planning, and treatment evaluation. Cognitive assessment is especially valuable in older adults for issues surrounding diagnoses and
treatment of neurodegenerative disorders (Lezak, Howieson & Loring, 2004; McKhann, Knopman & Chertkow, 2011). Nonetheless, cognitive
performance is influenced by various psychological factors that are essential to take into account for an accurate interpretation of test results.
Among those factors, depressive mood is well known to have a negative impact on cognitive functioning (Herrmann, Goodwin & Ebmeier,
2007; Lockwood et al., 2000; Bhalla et al., 2009; Boyle et al., 2010; Yeh et al., 2011).
Anxiety symptoms, which are more common than depressive symptoms, may also have an impact on cognitive functioning (Bryant,
Jackson & Ames, 2009; Grenier et al, 2011; Gum, King-Kallimanis & Kohn, 2009). However, regarding the latter, its relationship with cognitive
performance is poorly understood in older adults (Beaudreau & O’Hara, 2008). The study suggested that anxiety is associated with poor
inhibition and slowing processing speed however, there is not significant relationship discovered between word fluency and anxiety level.
Moreover, higher anxiety among older do not show significant relationship with all the executive functions as only inhibitory ability was
recognized with significant relationship with anxiety. According to the attention control theory, anxiety should have negative effects in
neuropsychological tests taxing inhibition and shifting between tasks (Eysenck et al,m 2007). This hypothesis is partially supported by data in
older adults (Beaudreau & O’Hara, 2009; Hogan, 2003; Pietrzak et al., 2012; Wetherell, et al., 2002). For example, anxiety symptoms measured
by the Beck Anxiety Inventory were negatively associated with performance on tasks measuring inhibition and processing speed/shifting
attention, but not verbal fluency (Beaudreau & O’Hara, 2009), and higher trait anxiety was linked to worse divided attention but better selective
attention (Hogan, 2003).
However, other results do not support the attention control theory. A recent study found that older adults with mild worry, according to
the Penn State Worry Questionnaire, were poorer than elders with minimal worry on tasks assessing visual attention and spatial memory

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(Pietrzak et al., 2012). Data from the Longitudinal Aging Study Amsterdam also indicated that anxiety symptoms assessed by the Hospital
Anxiety and Depression Scale-Anxiety subscale (HADS-A) have both positive and negative impact on cognitive functioning depending on
anxiety level (Bierman et al., 2005; Bierman et al., 2008). A curvilinear relationship was observed between anxiety and performance for most
cognitive functions, mild anxiety being beneficial whereas high anxiety having no or negative impact. In addition, the impact of anxiety on
cognitive functioning seems to be strongly influenced by other variables, especially depressive symptoms (Bierman et al., 2005; Bierman et al.,
2008). It was reported that adjusting for depressive symptoms cancels or reverses the negative effect of anxiety on cognitive performance that
high trait anxiety is beneficial for cognitive performance in older men (Bierman et al., 2005; Bierman et al., 2008; Biringer et al., 2005).
Depression in the absence of anxiety does not significantly affect cognitive functions. However, people experiencing both anxiety and
depression recognized less performance in 3 areas, processing speed, semantic memory and episodic memory which clearly presents that both
anxiety and depression leads to cause cognitive dysfunction (Beaudreau & O’Hara, 2008). Thus, the current section clearly evaluates and
examines anxiety, depression and cognition decline in the young and elderly adults.
Anxiety and Cognitive Function
Emotion plays a leading role in memory processes at all ages. The emotional regulations has been increasingly investigated by many
researchers. Cisler and Olatunji (2013), researched the relationship between emotion regulation and anxiety disorders. The study clearly stated
that emotional regulation is a multidimensional construct refers to heterogeneous actions, which modulate emotions and their expression. It
explains the maintenance of anxiety disorder that cannot be demonstrated simply as a problem of too much anxiety and one’s ability and
capacity to modulate emotions are necessary to develop a deep understanding of the etiology and treatment of anxiety. The study suggested that
maladaptive patterns of an individual’s emotion regulation characterize their anxiety disorder, particularly with generalized anxiety disorder.
According to the study results, emotion dysregulation model defined GAD as a means of experiencing emotions quickly at greater
intensity. Its emotional reactivity makes it tough for the people to regulate and understand their emotions. Such problem faced by an individual
leads to cause anxiety, worry and depression and contributes towards panic disorder. The study found that although, sensitivity of anxiety (AS)
constitutes risk factors that cause panic disorder, but at the same time, whether or not AS resultant panic disorder depends upon emotion
regularity.
Likewise, the findings of Kashdan, Zvolensky and McLeish (2008), stated that people with high level of AS, worry, anxious arousal
heightened due to less acceptance distress. Agoraphobic cognitions heightened in presence higher emotional expressiveness. Lack of emotional
clarity, limited access to its regulation and lacking emotional acceptance develop g; from hyperarousal to difficulties with concentrating. This
latter effect posttraumatic stress disorder (PTSD). Thus, it becomes clear that emotion is an important or critical determinant of onset and anxiety
disorder maintenance.
Vytal and et.al. (2013), investigated the relationship between anxiety and cognitive functions and the findings reported that anxiety
impair working memory of an individual when he or she feel anxious. It impairs both verbal and spatial working memory processes. However,
considering the cognitive load, it has different impact on the performance because the results suggested that low-load verbal working memory is
highly susceptible with respect to anxiety disruption whereas spatial WM is disrupted regardless task difficulty.
Yang and et.al., (2015), research evident that cognitive impairment in GAD and as per the results, people with GAD are more likely to
impair or disrupt their cognitive functionality, more importantly, selective attention as well as working memory. The study based on neutral
stimuli found that lower foal-focused attention with GAD is an general deficit regardless emotional content. Despite this, Robinson and et.al.
(2013), research results reported that both translational anxiety induction as well as pathological anxiety promote harmful stimuli among
individual at distinctive cognition level comprising perception, attention, working memory and executive function as well.
Kensinger and Corkin (2003), for example, found that in young adults negative stimuli generated more robust long-term memory than
emotionally neutral stimuli. Even so, the negative stimuli did not affect accuracy and tended to increase overall reaction times. Notable,
however, is that the Kensinger and Corkin (2003) study did not compare results from positive emotional stimuli, nor did they compare older
adults to younger adults. Chainay et al. (2012) compared young adults’ recall of positive, negative, and neutral stimuli in categorization and

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recognition tasks and found that emotional stimuli generated more reliable responses in recognition tasks but had little difference in
categorization tasks, indicating the intention of the participant to later recall (as opposed to simply categorize) impacted whether emotional
content mattered. Studies by Martinez-Galindo and Cansino (2015) supported that claim by noting that in healthy individuals, faces presented in
a positive context were more likely to be recalled than those in a negative context or an emotion-neutral context. Martinez-Galindo and Cansino
(2015) recorded brain autonomic responses to the stimuli and found that stronger autonomic responses were directly tied to positive emotional
responses, but not to negative or neutral responses. In particular, N170, P170, occipital P300 and frontal SW demonstrated these valence effects,
while the N300 and occipital SW were modulated by emotion (Martinez-Galindo & Cansino, 2015). This evidence indicates that the emotional
responses generated more efficient neural recording processes enabling better memory storage and stronger memory consolidation (Martinez-
Galindo & Cansino, 2015).
In a series of experiments with young adults, Zimmerman and Kelley (2010) found that participants expected to remember events that
were more emotional compared to events that were emotionally neutral. However, the expectations were significantly overconfident in the cases
of negative events compared to positive ones, indicating a recall bias in which positive events were more consistently retained compared to
negative ones (Zimmerman and Kelley, 2010). A similar experiment comparing emotional recall and event-related potentials in both younger
and older adults found that younger adults were more responsive to negative events than older adults (Molnár et al., 2013). Molnár et al. (2013)
also noted that the late positive complex neurological responses (LPC) were sensitive to the emotional content or valence of the stimuli, and
larger for more emotional stimuli than neutral ones. The LPC typically has been reported to have a latency of about 500 to 800 ms. with a centro-
parietal distribution. Molnár et al. (2013) found that older adults had an LPC distribution primarily in the frontal area for all emotional contents
(i.e., positive, neutral, negative), while the LPC had a centro-parietal distribution in the younger adults.
Healthy older adults have expanding emotional control over their cognition and memory as compared to younger adults. Charles, Mather
and Carstensen (2003) found that healthy older adults recalled emotional memories more easily than emotion-neutral events and that greater
emotional information was retained and recalled as individuals aged. In a study that measured differences in recall among positive, negative, and
emotionally neutral images, Charles et al. (2003) found that while overall recall function declined in older adults compared to younger or
middle-aged adults, the degree to which emotional content, in particular positive emotional content, was retained increased significantly with
age. Furthermore, the overall proportion of images recalled only slightly declined between young and old adults, but the proportion of negative
images recalled declined more than the positive ones with older adults, indicating a preferential bias for retaining positive emotional content in
healthy older adults.
The neurobiology of healthy elderly adults with respect to memory and cognitive tasks changes compared to younger adults. Older adults
often show greater symmetry in fMRI studies of prefrontal cortex responses both when encoding new data and when retrieving data (Olichney et
al., 2010). In contrast, only a few recent studies have shown age-related differences in the medial temporal lobe (MTL) activation, despite
deterioration from MTL atrophy and early-stage Alzheimer’s disease, both of which occur before noticeable declines in memory functioning
(Olichney et al., 2010). Olichney et al. (2010) divided a group of 17 cognitively normal, right-handed healthy older adults into high-functioning
and low-functioning based on their performance on a cued-recall task. Olichney et al. (2010) used both fMRI data and blood-oxygen level
dependent responses (BOLD) in a word repetition task that involved executive functions including attention, perceptual, cognitive, and episodic
memory processes as well as motor responses. The high-functioning group showed distinct differences in BOLD and fMRI patterns compared to
the low-functioning group with the high-functioning seniors having faster and more extensive differential BOLD responses than the lower-
functioning group, despite the fact that the the two groups were both considered healthy with no obvious mental decline. Olichney et al. (2010)

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noted that despite memory recall accuracy being the same between the two groups, the differences in neural functioning may reflect significant
differences in clinical cognitive decline that are as yet asymptomatic.
Anxiety and Cognitive Impairment
Cognitive impairment consists of various levels. Subjective cognitive impairment (SCI) refers to a perceived decline in cognitive
function when objective measures of cognitive impairment note no such deficit (Hill et al., 2016). Objective cognitive impairment (OCI) is a key
factor in diagnosing Mild Cognitive Impairment (MCI). This occurs when there is sufficient change in cognitive functioning to result in
performance decline in objective cognitive tests. Dementia is more extreme still, with significant declines in cognitive functioning. Although
there are numerous causes of dementia, the most commonly cited is Alzheimer’s disease, though there are many other causes as well. This
section will discuss these various impairments in increasing order of severity.
Most of the researches reveal that anxiety coexists with the cognitive impairment and they follow symbiotic relationship with each other.
As per Peterson (2015), anxiety is more common in late-life psychiatric diagnosis in older adults, surpassing mood disorders and severe
cognitive impact. As people grow older, they have plenty of things in their minds causes stress and anxiety. For instance, worrying about family
members and other concerns combines with changing brain that accelerate cognitive disorders i.e. dementia and others. The research found a
clear relationship between higher anxiety symptoms and cognitive decrease in adults. Accumulation of clumps in the brain eventually leads to
decline cognitive memory and mild cognitive impairment. In this, cognition health of the test population was tested at three baseline point, 18,
36 and 54 months time interval for carrying out longitudinal study. People with higher amyloid plaques at baseline presented negative impact on
the language, memory, attention, visuospatial ability and others. However, under the same group, individuals with greater anxiety symptoms
found considerable greater decline in the global cognition, verbal memory, executive function, language and others comparison to those with less
anxiety. Thus, the study presents that older adults who have positive amyloid scan with anxiety symptoms resents a rapid decrease in cognition,
language, verbal memory, executive functions and others.
Anxiety, SCI and OCI
SCI typically, this comes in the form of memory issues and forgetfulness. Takeda et al. (2008) noted that many people experience this
subjective decline in memory beginning commonly in middle age (i.e., their 40s), but no psychometric test yet measures SCI clearly.
Furthermore, SCI is associated with increased risk of developing dementia and impacts the individual’s quality of life (Tales et al., 2015). In a
longitudinal study of non-demented individuals with SCI over the age of 75, Luck et al. (2015) found that approximately 12.3% of 953
individuals in the study experienced SCI and these individuals had a significantly higher mortality rates (114.8 per 1000 person-years, vs. 71.7
per 1000 person-years for non-SCI individuals). Furthermore, Luck et al. (2015) found that those experiencing SCI had a survival time nearly
1.5 years shorter than those who did not experience SCI, with SCI increasing the mortality risk by about 50%. These results are supported by a
study by Mitchell et al. (2014) who conducted a meta-analysis of nearly 30 thousand individuals with a baseline age averaging 71.6 years. About
half complained of SCI symptoms at baseline. Of those, 14% developed dementia over the four years of the study and 26.6% developed mild
cognitive impairment (i.e., objective cognitive impairment) (Mitchell et al., 2014). The risk of developing dementia in those complaining of SCI
at baseline over the 4-year study was twice the risk of those who did not complain of SCI (Mitchell et al., 2014). SCI is thus not an illusory
experience, but is instead something that merits study. Pennington et al. (2015) also found that SCI can have important impacts on such life
aspects as employment and can result in inappropriate diagnoses and treatments.
SCI appears to reflect specific changes in neuroanatomy that may not reflect in behavioral tests. Perrotin et al. (2015) conducted T1-
weighted MRI and high-resolution MRI proton-density hippocampal sequences on healthy individuals with and without SCI. Perrotin et al.
(2015) found that hippocampal subfield changes existed in SCI individuals that were similar to the changes found in individuals with

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Alzheimer’s disease, a comparison not found in individuals not experiencing SCI. A separate study of patients complaining of SCI who test
normal under standard cognitive tests compared to a matched group of non-SCI controls found that SCI individuals have higher brain amyloid-
beta (Aβ) deposits (Snitz et al., 2015). This study, unlike most, used participants who actively complained (i.e., to a memory clinic) of SCI
symptoms, rather than those who merely answered positively in a laboratory-based questionnaire in a research setting. Confounding studies to
the Snitz et al. (2015) results do exist, including Hollands et al. (2015), who found no such higher Aβ deposits. However, the Hollands et al.
(2015) recruited participants using research questionnaires rather than from patients complaining of SCI at a memory clinic. Snitz et al. (2015)
theorized that there may be significant differences between those patients who complained vs. those who answered a questionnaire in a research
setting, and this may account for differing research results.
Routine memory tests are inadequate to measure SCI because they are intended for more advanced cognitive losses (Takeda et al., 2008).
Guillo-Benarous et al. (2014) has begun to address this with preliminary work comparing those with SCI complaints across a battery of
psychological, memory, and demographic assessments, but few conclusions have yet been published of their results.
Of interest is whether SCI may be a precursor for future declines in cognition or Alzheimer’s disease, and thus whether the individual
perceives symptoms that cannot yet be objectively measured. Other factors that may contribute to SCI are the presence of comorbidities such as
anxiety or depression (Hill et al., 2016). In a review of the literature, Hill et al. (2016) found that most longitudinal studies of SCI were more
likely to link baseline presence SCI to development of depression when measured again at 2-year, 4-year, and 10-year follow-ups. This result
also appeared to be positively correlated between the number of SCI complaints at baseline to risk of depression at follow-up (Hill et al., 2016).
A confounding study noted by Hill et al. (2016) found the link between SCI and depression missing, but a positive association between SCI and
anxiety at baseline along with greater increase in anxiety over time compared to participants with no SCI at baseline. Furthermore, SCI
complaints were found to decrease when participants attended a memory clinic between baseline and follow-up periods (Hill et al., 2016). One
important caveat mentioned by Hill et al. (2016) was that it was unclear if the SCI complaints caused depressive symptoms or were caused by
such symptoms. A French study of nearly 9300 French, community-living adults at least 65 years old found that SCI, specifically in the form of
subjective memory impairment, was significantly associated with scores on a visual retention test, without regard for social activity, social
support, or education levels (Genziani et al., 2013).
OCI refers to measurable cognitive declines, either as a result of ageing, illness such as Alzheimer’s or diabetes, or psychological
impairment such as anxiety or depression. Objective cognitive declines have been associated with declines in sensory processing, such as loss of
hearing (Lin et al., 2011). The key difference between SCI and OCI is that OCI is measurable using a variety of instruments, including the
MoCA and others. Lin et al. (2011) conducted a 10-year follow-up of a study of 1884 members of the general population, in their mid-60s at the
time of the follow-u to determine demographic, lifestyle, and data on cognitive impairments, sensory impairments, and other issues. Lin et al.
(2011) found, after adjusting for age, sex, and education, that risk of OCI was higher ten years after baseline for those with baseline hearing
impairments, visual impairments, or olfactory impairments, all at a statistically significant level. Each was also found to be an independent factor
in development of OCI (Lin et al., 2011).
One of the most interesting studies from Caracciolo et al. (2012) was a nationwide study of nearly 12,000 twins at least 65 years old,
comparing the prevalence of SCI and OCI with no dementia. The rate of SCI was 39% and the rate of OCI with no dementia was 25%, with both
groups older than those with no cognitive impairments at all. Those with OCI also were found to be more likely unmarried, lower socioeconomic
status (Caracciolo et al., 2012). Those with SCI differed from the OCI twins by more likely being more educated, more likely to be married, and
with a higher socioeconomic status. Caraccilo e t al. (2012) concluded that the co-occurrence of SCI and OCI was likely more heavily influenced
by shared environmental factors rather than genetics.

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Cooper et al. (2015) conducted a meta-analysis of longitudinal studies of mild cognitive decline that reported risk factors for OCI
developing into dementia. Cooper et al. (2015) concluded that some risk factors are modifiable. For example, diabetes increased the risk of
developing dementia, as did pre-diabetes, metabolic syndrome, neuropsychiatric symptoms including anxiety and depression, and low levels of
dietary foliate. Cooper et al. (2015) suggested that dietary interventions to control foliate levels, diabetes and pre-diabetes conditions, plus
interventions to reduce depression and anxiety as well as other neuropsychiatric conditions might reduce the conversion rate from OCI to
dementia.
OCI is a key criterion for the formal diagnosis of Mild Cognitive Impairment (MCI) in the fifth edition of the Diagnostic and Statistical
Manual of Mental Disorders (DSM-V) (American Psychiatric Association, 2013). The total criteria for a diagnosis of MCI includes self- or
informant-reported cognitive complaints, OCI, preserved independence in functional abilities, and no dementia present (APA, 2013). Reijnders,
van Heugten, and van Boxtel (2013) conducted a meta-analysis of treatments for MCI to determine what types of treatments improve MCI. Their
results indicated that cognitive training could improve objective cognitive functioning, memory performance, executive functioning, processing
speed, and similar abilities, but found little evidence that such training provided measurable positive impacts on daily living activities (Reijnders
et al., 2013). OCI appears to have multiple variations, depending on the specific cognitive functions impaired. Bondi et al. (2014) identified three
specific subtypes using participants diagnosed as having MCI: those most severely impaired in language, memory, or executive functions, with
considerable overlap among those three areas of impairment. Bondi et al. (2014) also noted that regardless of the specific impairments suffered,
those diagnosed with MCI either remained at that level or progressed to dementia, had abnormal CSF levels of Aβ, and p-tau biomarkers, both
associated also with Alzheimer’s disease. Bondi et al. (2014) also found that those diagnosed with MCI based on the use of the criteria defined in
the Alzheimer’s Disease Neuroimaging Initiative (ADNI)—based primarily on a single memory test plus some self-ratings and clinical
judgments—were frequently misdiagnosed and given a false-positive diagnosis of MCI.
MCI is also closely associated with depression and especially with anxiety. A Rotterdam population-based study found that those
diagnosed with MCI had more than double the risk of developing anxiety with an odds ratio of 2.59 (95%, confidence interval 1.31, 5.32).
(Mirza et al., 2017). Evidence indicates that MCI is a condition that includes a neurodegenerative pathology but primarily manifests in
psychiatric symptoms. Mirza et al. (2017) argued that patients given an MCI diagnosis are aware that this is likely to degenerate into all-out
dementia, thus prompting the development of depression and/or anxiety. Astell et al. (2016) conducted a mixed-methods study of patients given
a diagnosis of MCI and found that interviews with patients and caregivers resulted in substantial revelations of anxiety about the meaning of the
diagnosis and fears for the future for themselves and their families.
The relationship between MCI and anxiety has neurophysiological basis. Mah, Binns and Steffens (2015) conducted a 36-month study of
376 MCI-diagnosed individuals, including assessments of cognitive functioning and MRI studies of hippocampal, amygdalar, entorhinal cortical
volumes and thickness. Mah et al. (2015) found that anxiety severity increased the rate at which MCI converted to full-out Alzheimer’s disease,
even after controlling for depression and cognitive decline. Greater anxiety also predicted faster declines in entorhinal cortical volumes, but was
not quite statistically significant for entorhinal cortical thickness (Mah et al., 2015). The implications of this study are that anxiety is not a
prodromal noncongitive effect of Alzheimer’s disease, but instead is a factor that may accelerate the decline from MCI into dementia via
changes, either direct or indirect, on the entorhinal cortex (Mah et al., 2015).

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Anxiety and Dementia
Ganguli (2009), studied the relationship between depression, cognitive impairments and dementia. It is more common among older
adults, it is because, over the age, people cognitive functioning declines even depression itself is a reason responsible for cognitive impairment
and dementia. People experiencing Alzheimer and other kind of dementia suffers health problems and behavioral symptoms which causes
cognitive difficulties. Meta analysis suggested that depression itself is a cause of dementia. The temporal relationship between depression and
cognitive ability in elderly people varies widely. Although, depression is found as a risk factor preceded symptoms of dementia however, on the
other side, it seems as a prodrome instead of dementia predictor. It means depression might be an early warning indicator of dementia.
Depression is a psychological response to the individual’s self-awareness of MCD. at an early stage, patient experiencing dementia may
feel some changes and they feel difficulties in their routine tasks and activities. Withdrawing such activities is a natural response to it which lead
others to think that such person is experiencing anhedonia and depression. Depressive symptoms may cause disease of dementia with memory
loss, brain disease and others affecting noradrenergic and serotonergic systems as well. People with progressive result of brain disease manifest
dementia. Besides this, people who are experiencing greater cognitive deficit could feel depressed resultant poor prognosis.
Morimoto and et.al. (2015), studied cognitive impairment in depressed older adults and its results estimates depression in dementing
disorders from 30% to 50% . It recognized depression in Azheminer’s disease notably a prominent disturbance in motivation, fatigue, apathy and
others. People with dementia including Parkinson’s disease and vascular dementia are found more likely to experience depression in comparison
to patients with AD. In late life, depression is found as a prodrome of dementia, it is because, depressed mood is favorably associated with
cognitive decline and higher risk of dementia due to the presence of severity, length of depression and onset age. Impairment in episodic
memory, verbal fluency, visuospatial skills, psychomotor speed in late-life develop first behavior difficulties or abnormalities as a risk of
dementia.
As per the findings of Bardrakalimuthu and Tarbuck (2012), anxiety may be caused by various circumstances that negatively impact the
individual is thinking ability. In the study, 38% to 72% people with dementia are found with anxiety which adversely impact their cognitive
functioning with poor life quality. Thus, anxiety is seen as a hidden element in dementia. It seems difficult to present anxiety in people with
dementia, more importantly, when receptive or expressive speech is impaired. Anxiety in elder adults with Mild Cognitive Impairment (MCI)
found that people with GAD feel more psychological and behavioral difficulties, anxiety, depression, agitation and sleep disorder. Higher the
level of anxiety in MCi adversely affects executive functionality. Presence of anxiety in dementia is very difficult to control. The study stated
that anxiety, apathy, depression are several common feel experiences by the people with dementia which affects their mental health and
emotions.
Relationship Between Anxiety, Depression and Cognitive Impairment
Anxiety and depression are correlated to each other and they are banded about one’s own illness. A study conducted by Bowman (2017),
reported that 16-50% individual with depression also have Generalized anxiety disorder. It is because, according to NHS, the two separate
conditions share some common symptoms like feeling excessive worry, trouble concentrating and others. Anxiety manifests with energy
abundance whereas depression shows lethargy. Both of these leads to bring change in neurotransmitter function. Low level of serotonin,
dopamine and epinephrine plays an important role in depression and anxiety. Anxiety disorder in an individual is a cause of depression, it is
because, intensive anxiety symptoms provide a hopelessness feeling that lead to cause depression and have severe and long-lasting impact.
Similarly, Tracy (2015), study states that depression is a situation of low energy, anxiety, on the other hand, is a situation of high energy,
still, both are directly related to each other. An extremely depressed individual experiences anxiety which even leads to cause panic attacks.
Having such panic disorder itself is a reason of depression, more important, among those individuals with low control our lives. Although both
are different in many aspects, still, depression lead to develop negative attitude such as hopelessness, despairing and anger resultant less energy

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level. As a result, over the period, depressed people experiences overwhelmed in their daily tasks and activities. However, an individual with
GAD experiences panic, anxious, threatened, fear in obvious situations where other people do not feel the same. Such people experiences sudden
panic attacks without any recognized trigger and lives life with anxious and constantly nagging worry. If both of these are not well treated, then
it restricts individual’s working capabilities, maintain social relationship and others. Similar treatments are applied for both of these two
disorders. Antidepressant medication is used for depression, anxiety as well as behavioral therapy. People with depression diagnosed with GAD
and several with panic disorder too. Other anxiety disorders comprises obserssive-compulsive disorder, post-traumatic stress disorder, as both
the factors go hand by hand, therefore, considered as fraternal twins. Having both anxiety and depression together is a tremendous challenge as
clinicians had observed that anxiety occurs together with the depression. A person with GAD suffers “fear itself” even in the normal situation
when there is no real threat exists.
Research conducted by Trachy (2015), evident that depression not only causes emotional challenges and physical symptoms but also
leads to cause cognitive dysfunction, which is a bipolar disorder in schizophrenia. Cognitive function is an intellectual skills or thinking ability
that allow an individual to acquire, perceive and respond information. Such skills include memory, attention ability and solve communication
problems and the ability to recognize and act on the information. Due to less level of motivation which is often seen in depressed people, it is
believed that people do not feel motivated and encouraged to put great efforts in accomplishing their cognitive tasks resultant cognitive
dysfunction and deficit. Even in the treatment, in such circumstances when depressed people are suggested to take high dose of
antidepressant/antipsychotic they may experience worse situation and cognitive deficit symptoms. Therefore, doctors requires to treat patient
carefully to prevent such occurrence. Drug and other alcoholic abuse, which is one of the most important reasons of depression, can cause
cognitive impairment.
However, on the other side, Beaudraeau and Hara (2009), reported that older people endorsing higher depression symptoms indicate
larger depressive symptoms and processing speed. Anxiety and depression together leads to impact cognitive function but in distinguish manner
in older adults than clinically diagnosed people. The study discovered that coexistence of anxiety and depression together is directly associated
with lower performance on 3 cognitive domains including episodic memory, shifting attention and semantic memory. Evidencing from the
results, it is determined that older psychiatric patients discovered significant overlap in cognitive deficit with either GAD or other major
depression.
Cognition is widely accepted to correlate to physical and mental health co-morbidities. Yates, Clare and Woods (2017) reported on a
study that investigated the interrelationships among health, mood, and cognition among older adults without dementia, comparing non-
cognitively impaired group with two matched groups of MCI with SCI, and MSC without SCI. The study found that while anxiety had a non-
significant statistical relationship with health, and cognitive status impacted anxiety, depression had a statistically significant relationship with
both cognitive status and health (Yates et al., 2017). As defined in this study, health scores were determined by healthcare use, a total health
score, and self-reported perceptions of quality of health. Increasing cognitive impairment was associated with poorer health scores, greater
depression, and more anxiety (though the last was not statistically significant). A neuropsychological investigation of anxiety in ageing and
dementia illuminates several complex factors related to ageing as a specific life stage with unique challenges. According to Erik Erikson’s
psychosocial theory, this developmental period is the eighth stage in a person’s life, which he characterized as “Integrity versus Despair”
(Erikson, 1982). While integrity comprises the perceptions of the individual who has lived a positive, moral life filled with economic, social,
and personal success, in all too many older adults there may be feelings of despair due to various disappointments and the corresponding sense
of major defeat. These psychosocial issues are generally found to be a primary focus in the “post-retirement elder years, typically after age 65”
(Hearn et al., 2012, p. 1). It may be a time in a person’s life when isolation or loneliness sets in, and in many cases the medical community and
professional health team will be vitally important to provide perhaps the only source of support and advice available.

12
Methods
This section briefly describes the methods used to conduct the investigation, including participants, measuring instruments used, and
other details of how the research was conducted.
Ethical Considerations
This study was conducted with the guidance and approval from the Research Ethics Committee at the Swansea University Department of
Psychology, which mandates informed consent of all participants, along with their rights to withdraw from the study at any time. The informed
consent form was signed by all participants. All data collected in this study was blinded to participant identity and stored under password
protection on the researcher’s computer. All the data will keep confidential which is only accessible by responsible authorities. The participants
were provided with a coded identification code to allow them to identify their personal test results while protecting their identities. All data
collected was used for empirical research, and not for any medical purpose.
Participants
Two sets of participants were chosen for the conduct of this test, an older group and a younger group. In the younger group, those who
participated received 6 credits; older group participants received transportation expense assistance as compensation for their participation.
Participants who exhibited severe depression were excluded. Other exclusions included poor self-reported general health; past history of head
injury or neurological, medical, or psychological problems; reported subjective cognitive impairment; vision not normal or corrected to normal;
and self-reported medications that impact cognitive functioning. Two males were excluded from the younger group and one male excluded from
the older group due to severe depression scores.
The younger group consisted of 52 undergraduates chosen from the psychology department at Swansea University, with 31 females and
21 males, aged between 18 and 25 years at a mean age of 19.92 yars and standard deviation of 1.57. These participants were selected from those
who signed up form the Psychology Subject Pool system. The older group of participants consisted of 52 randomly chosen from the general
public from those between the ages of 50 and 80, 32 females and 20 male at mean of 66.47 and standard deviation of 4.52, with one male
excluded. The age range of the older group was between 50 and 80 years. Participant recruiting was conducted via telephone and emails
containing experiment background information. Those who indicated willingness to participate received travel expenses to the experimental
venue if needed. All participants received university-approved informed consent prior to the start of the study.
Data Collection and Instruments
Data on participant demographics were collected via an information form the participants filled out. The data collected included age,
gender, marital status, current/past occupation, highest level of education, handedness, vision, and overall health information. This information
sheet also provided participants with a description of the test, and that the goal was to conduct a neuropsychological investigation of the
relationship between anxiety and cognitive function. Debriefing form is a consent form that is utilized in order to deliver information about the
study once it is done. It often uses in psychological researches after concluding the experimental results. Referring the current study, debriefing
form is used to fully inform all the participants about the ethical consideration to create any physical or psychological harm in any way in the
experiment.
A copy of the demographics form, information sheet and debriefing form seems found in Appendix A.

13
In addition to the demographic data collected, participants in this study took the Beck Anxiety Inventory (BAI) , the Beck Depression
Inventory (BDI), the Montreal Cognitive Assessment (MoCA) version 7.1, the Prospective-Retrospective Memory Questionnaire (PRMQ), and
the State Trait Anxiety Inventory (STAI) in full, including both the State and Trait subsections (STAI-S and STAI-T). Each of these instruments
is described below.
Beck Anxiety Inventory (BAI)
The Beck Anxiety Inventory (BAI) was used to determine participant anxiety levels (Liang, Wang and Zhu, 2016). This test is a 21-item
self-assessment using a four-point Likert scale (0: “not at all” to 3: “severely”) that focuses on somatic symptoms of anxiety as a way of
distinguishing between anxiety and depression (Julian, 2011). Scoring for the BAI is computed by adding the scores of the 21 items, and thus
ranges from 0 to 63, with higher scores indicating greater anxiety levels. A score between from 0–21 indicates no to mild anxiety; a score
between 22–35 indicates moderate anxiety; and a score between 36–63 indicates potentially severe anxiety (Beck, 1988). Validity of the BAI is
shown with its good correlations with other measures of anxiety such as the Hamilton Anxiety Rating Scale, the State-Trait Anxiety Inventory
(STAI) and the anxiety scale of the Symptom Checklist (Julian, 2011). Reliability of the BAI has been shown with high internal consistency as
measured by Cronbach’s alpha (0.90 to 0.94). Test-retest also provides reasonable correlations in the BAI (Julian, 2011).
Beck Depression Inventory (BDI)
The BDI is a 21-element self-reporting scale using a four-choice Likert scale (ranked from 0 to 3). The possible scores thus range from 0
to 63, with higher scores indicating greater or more severe depression (de Oliveira and et.al., 2014). The questions in the BDI focus on cognitive
distortions common in those with depressive symptoms, such as “I blame myself for everything bad that happens” (Farinde, 2013). It is designed
for use by individuals at least 13 years old, with scores greater than 21 indicating clinical depression, and scores above 30 indicating severe
depression. The BDI is designed to be simple to use with a variety of populations and quick to administer, taking 5 to 10 minutes only (Farinde,
2013). The BDI has been demonstrated to be valid and reliable in both adolescent populations and with the elderly (adolescents: Kauth & Zettle,
1990; elderly: Penk & Robinowitz, 1987; Scogin et al., 1988; Wetherall & Gatz, 2005). Comparison studies of the BDI in general populations
have also demonstrated high levels of both reliability and validity (Aalto et al., 2012). Internal consistency of the BDI has been demonstrated
alphas approximating 0.91, and reliability in test-retest results over a one-week period of 0.93.
Montreal Cognitive Assessment (MoCA)
The MoCA is designed to detect objective cognitive functioning and mild cognitive impairment and assesses such cognitive domains as
attention, concentration, executive functions, memory, language, visuospatial skills, abstraction, calculation, and orientation (Julayonont et al.,
2013). The instrument consists of a variety of verbal and pencil-and-paper tasks such as drawing a clock, copying a diagram of a cube, and doing
delayed verbal recall of a list of words. Scoring ranges from 0 to 30, with higher scores indicating less cognitive impairment (Julayanont and
Nasreddine, 2017). Typical cutoff points for normal cognitive functioning are approximately 25, but can range as low as 23 in some studies
(Julayanont et al., 2013). The MoCA is commonly used as a screening tool to detect cognitive impairment from Alzheimer’s disease. The
sensitivity of the MoCA for Alzheimer detection averages 86% across a number of studies since 2005, with a range of sensitivity of between
77% and 96% (Julayonont et al., 2013).

14
Prospective-Retrospective Memory Questionnaire (PRMQ)
The PRMQ is a self-reported instrument that measures prospective and retrospective memory slips in ordinary living activities. The
instrument includes 16 items, each with five Likert-scale responses ranging from “very often” (scored as a 5) to “never” (scored as a 1) in
response to questions such as “Do you forget something that you were told a few minutes before?” Half of the questions refer to retrospective
memory errors and half to prospective memory errors. Scores thus range from 16 to 80. The reliability of the PRMQ has been estimated at 0.89
overall and 0.84 for prospective scale and 0.80 for the retrospective scale (Crawford et al., 2003). The PRMQ has not shown to have any
statistically significant variances due to either gender or age, making it appropriate for use both with young adult and older adult populations
(Crawford et al., 2003). For non-clinical populations, the PRMQ has shown mean total scores of 38.88 (s.d. 9.15), mean prospective scores on 8
items of 20.18 (s.d. 4.91) and mean retrospective scores on 8 items of 18.69 (s.d. 4.98) (Crawford et al., 2003). In this study, scores from the
PRMQ are reported as “SelfQ” scores because this is a self-rated instrument (Mefoh and Ezeh, 2016)
State Trait Anxiety Inventory (STAI)
The STAI is a commonly used measure of the intensity of feelings of anxiety, differentiating between current-state anxiety in the present
moment and trait anxiety that is a general tendency to perceive situations as threatening or anxiety-producing (McDowell, 2006). The full STAI
has two separate 20-item scales, the STAI-S Anxiety scale that evaluates current state of anxiety, and the STAI-T Anxiety scale that evaluates
general, long-lasting feelings of anxiety (Dennis, Coghlan and Vigod, 2013). The STAI has been demonstrated as highly reliable in both scales,
and across a variety of samples, including older adults, working adults, general population, students, and others (McDowell, 2006). The STAI
has also been shown to be valid, with some data suggesting that the STAI and the BAI may measure different factors of anxiety (McDowell,
2006). In studies of young adults, the validity comparison between the BAI and the sister measure BDI, the STAI correlated more closely with
BDI than with BAI, implying that the STAI is actually a closer measure of depression than anxiety (McDowell, 2006).

15
RESULTS
Agegroup N Mean Std. Deviation Std. Error Mean
BAI (50-80) older adults 52 6.4423 5.92573 .82175
(18-25)Young group 52 13.4231 9.91799 1.37538
STATE ANXIETY (50-80) older adults 52 29.6154 10.97440 1.52188
(18-25)Young group 52 38.0769 11.66837 1.61811
TRAIT ANXIETY (50-80) older adults 52 34.4423 8.62148 1.19558
(18-25)Young group 52 43.5577 11.42651 1.58457
It can be seen from the table that BAI score is deviating more in case of young people then old ones. However, mean score is low for old
adults then young people. Similarly, in case of state anxiety higher standard deviation is observed in case of young then old people. Mean score
is high for young then old people. Trait anxiety is deviating at high rate in case of young people relative to old people. It can be said that
deviation in case of anxiety score is higher for young people then male.
Gender N Mean Std. Deviation Std. Error Mean
BAI Old Males 20 7.3000 7.53308 1.68445
Old Females 32 5.9063 4.71346 .83323
STATE ANXIETY Old Males 20 29.0000 7.44807 1.66544
Old Females 32 30.0000 12.79617 2.26206
TRAIT ANXIETY Old Males 20 34.9000 8.99649 2.01168
Old Females 32 34.1563 8.51227 1.50477
In respect to old male and females in BAI score high deviation is observed in case of male. However, in case of state anxiety score there
is high deviation in case of females then male. . Mean score is high in case of old males then old females in respect to BAI, state anxiety and trait
anxiety. Almost equal deviation is seen in case of male and females in trait anxiety score. It can be said that trends are not in specific direction.
Gender N Mean Std. Deviation Std. Error Mean
BAI Young Males 21 9.9048 10.81159 2.35928
Young Females 31 15.8065 8.64646 1.55295
STATE ANXIETY Young Males 21 36.0000 12.49000 2.72554
Young Females 31 39.4839 11.06306 1.98699
TRAIT ANXIETY Young Males 21 41.4286 12.99066 2.83479
Young Females 31 45.0000 10.20457 1.83280
In case of young male BAI score is deviating at fast rate then young female. In case of state anxiety and trait anxiety also it is observed
that values of variable deviate at fast rate in case of male then female. Hence, it can be said that anxiety score is deviating more in case of male
then female. Mean score is high for females then males across all three categories. Hence, it can be said that anxiety level is high in case of
female then male
We just need to write something before start anaizing the data (Like an intro for this section, please)
Descriptive statistics
It uses to describe the general or basic characteristics of the given data set. It includes central tendency measures and dispersion
measurement. Mean is the most appropriate measurement of central tendency that indicates average value whereas standard deviation shows
scatter or variability of each item from the mean score.
Table 1 Descriptive Statistics
Age-group N Minimum Maximum Mean Std. Deviation
(50-80) older adults
Age 52 55 78 66.58 4.500
Gender 52 1 2 1.62 .491
Valid N (listwise) 52
(18-25) Young group
Age 52 18 28 19.85 1.539
Gender 52 3 4 3.60 .495
Valid N (listwise) 52

16
Figure 1 Proportion of old male and old female Figure 2 Proportion of old male and old female
The mean age score in the older adults (M=66.58, SD = 4.5), however, the same for the younger group (M = 19.85 years, SD = 1.539).
Lower value of standard deviation for elder indicates that their age shows greater variability. However, gender category for elder adults is found
(M=1.62, SD = 0.491) and for youngsters (M = 3.60, SD = 0.495).
Table 2 Gender Distribution Of Elder and Younger Group
Agegroup Frequency Percent Valid Percent Cumulative Percent
(50-80) older adults Valid
Old Males 20 38.5 38.5 38.5
Old Females 32 61.5 61.5 100.0
Total 52 100.0 100.0
(18-25)Young group Valid
Young Males 21 40.4 40.4 40.4
Young Females 31 59.6 59.6 100.0
Total 52 100.0 100.0
Figure 2 Pie chart presenting gender classification for older adults Figure 4 Pie chart presenting gender classification for younger group
The results presented that in the older adults group, female had greater proportion against male (61.5%>38.5%). Likewise, in the young
group, female proportion is greater than young male proportion (59.6%>40.4%).
Table 3 Level of education for both younger and older group
Age group Frequency Percent Valid Percent Cumulative
Percent
(50-80) older adults Valid
GCSEs/O Levels 16 30.8 30.8 30.8
A LEVELS 3 5.8 5.8 36.5
HIGHER EDUCATION
CERTIFICATES 11 21.2 21.2 57.7
BACHLOR'S DEGREE 17 32.7 32.7 90.4
MASTER'S DEGREE 4 7.7 7.7 98.1
PhD 1 1.9 1.9 100.0
Total 52 100.0 100.0
(18-25) Young group Valid GCSEs/O Levels 3 5.8 5.8 5.8

17
A LEVELS 41 78.8 78.8 84.6
HIGHER EDUCATION
CERTIFICATES 4 7.7 7.7 92.3
BACHLOR'S DEGREE 4 7.7 7.7 100.0
Total 52 100.0 100.0
Among youngsters, majority of the people had completed their A level graduation (41, 78.8%), however, among elder adults, majority of
the audience base had completed bachelor’s degree (17, 32.7%) and GCSEs level (16, 30.8%).
Table 4 Marital Status of Young and Older Adults Group
Agegroup Frequency Percent Valid Percent Cumulative Percent
(50-80) older adults Valid
SINGLE 4 7.7 7.7 7.7
MARRIED 32 61.5 61.5 69.2
DIVORCED 11 21.2 21.2 90.4
WIDOWED 2 3.8 3.8 94.2
DOMESTIC PARTNER 2 3.8 3.8 98.1
SEPARATED 1 1.9 1.9 100.0
Total 52 100.0 100.0
(18-25) Young group Valid
SINGLE 49 94.2 94.2 94.2
MARRIED 1 1.9 1.9 96.2
DOMESTIC PARTNER 2 3.8 3.8 100.0
Total 52 100.0 100.0
Majority of the elder people are married (32, 61.5%), however, a great proportion of youngsters are still single (49, 94.2%).
Table 5 Descriptive Statistics for State anxiety, trait anxiety, BDI and MoCA -----
Agegroup N Minimum Maximum Mean Std. Deviation
(50-80) older adults
STATE ANXIETY 52 20.00 88.00 29.6154 10.97440
TRAIT ANXIETY 52 22.00 55.00 34.4423 8.62148
BDI Levels 52 1.00 2.00 1.1346 .34464
Valid N (listwise) 52
(18-25)Young group
STATE ANXIETY 52 21.00 67.00 38.0769 11.66837
TRAIT ANXIETY 52 22.00 64.00 43.5577 11.42651
BDILevels 52 1.00 4.00 1.8654 1.25290
Valid N (listwise) 52

18
The findings clearly demonstrated that state anxiety mean score among youngsters (M = 38.07, SD = 11.66) was greater than older adults
group (M = 29.61, SD = 10.97) in respect to same variable. Similarly, trait anxiety and BDI level among (18-25) young group people is (M =
43.55, SD = 11.42) and (M = 1.86, SD = 1.25) above the older people value of variables which are trait anxiety (M= 34.44, SD = 8.62) and
(M=1.13, SD = 0.34) respectively.
Inferential statistics
Inferential statistics are different from descriptive statistics that is use to infer some useful thing from the given database to draw
conclusion for the entire universe. It is use to make judgmental decisions that observed differences in-group is actually dependent on other
variable or it is just happened by chance. T-test, ANOVA and others are some of the tools that are preferably use to draw or infer importance
conclusion from the sample for population study.
Difference in the anxiety score among elder adults and young people
Hypothesis:
H0: There is no significant difference in the anxiety score among elderly adults and youngsters.
H1: There is significant difference in the anxiety score among elderly adults and youngsters.
Table 6 Mean and Standard deviation for anxiety score for elder and youngsters
Agegroup N Mean Std. Deviation Std. Error Mean
BAI (50-80) older adults 52 6.4423 5.92573 .82175
(18-25)Young group 52 13.4231 9.91799 1.37538
STATE ANXIETY (50-80) older adults 52 29.6154 10.97440 1.52188
(18-25)Young group 52 38.0769 11.66837 1.61811
TRAIT ANXIETY (50-80) older adults 52 34.4423 8.62148 1.19558
(18-25)Young group 52 43.5577 11.42651 1.58457
Table 7 Independent Sample T-test result
Levene's Test for
Equality of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
BAI
Equal variances
assumed
10.114 .002 -4.357 102 .000 -6.98077 1.60217 -10.15866 -3.80288
Equal variances
not assumed
-4.357 83.296 .000 -6.98077 1.60217 -10.16725 -3.79429
STATE
ANXIETY
Equal variances
assumed
2.865 .094 -3.809 102 .000 -8.46154 2.22135 -12.86757 -4.05550
Equal variances
not assumed
-3.809 101.619 .000 -8.46154 2.22135 -12.86777 -4.05531
TRAIT
ANXIETY
Equal variances
assumed
5.803 .018 -4.592 102 .000 -9.11538 1.98502 -13.05266 -5.17811
Equal variances
not assumed
-4.592 94.855 .000 -9.11538 1.98502 -13.05622 -5.17455
Group Statistics indicate higher mean score of BAI, state anxiety, trait anxiety and BDI for younger group, (M = 13.42, SD = 9.91), (M =
38.07, SD = 11.66) and (M = 43.55, SD = 11.42) than elder adults with (M=6.44, SD = 5.59), (M = 29.61, SD = 10.97) and (M = 34.44, SD =
8.62) respectively. However, standard deviation demonstrates higher scattered values for the youngsters with higher standard deviation.
Independent Sample T test reflect sig. value 0.00 for all the anxiety score including BAI, State anxiety, Trait anxiety and BDI show that young
and elderly adults people mean anxiety score is significantly different.
Anxiety level and elder adults male and female
H0: There is no significant difference in the anxiety score among elderly adults male and female.
H1: There is significant difference in the anxiety score among elderly adults male and female.

19
Table 8 Mean and Standard Deviation For Anxiety Score For Old Male and Female
Gender N Mean Std. Deviation Std. Error Mean
BAI Old Males 20 7.3000 7.53308 1.68445
Old Females 32 5.9063 4.71346 .83323
STATE ANXIETY Old Males 20 29.0000 7.44807 1.66544
Old Females 32 30.0000 12.79617 2.26206
TRAIT ANXIETY Old Males 20 34.9000 8.99649 2.01168
Old Females 32 34.1563 8.51227 1.50477
Table 9 Independent Sample T-test Results For Old Male and Female
Levene's Test
for Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval
of the Difference
Lower Upper
BAI
Equal
variances
assumed
6.917 .011 .823 50 .415 1.39375 1.69447 -2.00970 4.79720
Equal
variances not
assumed
.742 28.394 .464 1.39375 1.87927 -2.45335 5.24085
STATE
ANXIETY
Equal
variances
assumed
.717 .401 -.317 50 .753 -1.00000 3.15614 -7.33930 5.33930
Equal
variances not
assumed
-.356 49.828 .723 -1.00000 2.80902 -6.64257 4.64257
TRAIT
ANXIETY
Equal
variances
assumed
.073 .788 .300 50 .765 .74375 2.47972 -4.23692 5.72442
Equal
variances not
assumed
.296 38.771 .769 .74375 2.51221 -4.33862 5.82612
The Group Statistics table indicates that considering mean BAI score is greater for old male (M = 7.3, SD = 7.53), however, state anxiety
is recognized with greater mean score for female (M = 30, SD = 12.79) On the other side, trait anxiety average is found similar for both the male
and female. However, at 5% sig level, BAI, State anxiety and trait anxiety’s sig value is (t(50) = 0.823, p = 0.415), (t(50) = -0.317, P =0.753)
and (t(50) = 0.300, p = 0.765) above sig value 0.05 that shows that difference in the mean anxiety score between old male and old female is not
significant and occur by change only. Hence, on the basis of the findings, it supports that null hypothesis proven true and anxiety level does not
shows a significant difference among both the elderly male and female.
Anxiety level and elder young male and female
H0: There is no significant difference in the anxiety score among young male and female.
H1: There is significant difference in the anxiety score among elderly young male and female
Table 10 Mean and Standard deviation for anxiety score for younger male and female
Gender N Mean Std. Deviation Std. Error Mean
BAI Young Males 21 9.9048 10.81159 2.35928
Young Females 31 15.8065 8.64646 1.55295
STATE ANXIETY Young Males 21 36.0000 12.49000 2.72554
Young Females 31 39.4839 11.06306 1.98699
TRAIT ANXIETY Young Males 21 41.4286 12.99066 2.83479
Young Females 31 45.0000 10.20457 1.83280
Table 11 Independent Sample t-test results for younger male and female
Levene's Test
for Equality of
Variances
t-test for Equality of Means

20
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval
of the Difference
Lower Upper
BAI
Equal
variances
assumed
.160 .691 -2.182 50 .034 -5.90169 2.70514 -11.33512 -.46826
Equal
variances not
assumed
-2.089 36.515 .044 -5.90169 2.82451 -11.62726 -.17612
STATE
ANXIETY
Equal
variances
assumed
.632 .430 -1.058 50 .295 -3.48387 3.29395 -10.09996 3.13222
Equal
variances not
assumed
-1.033 39.475 .308 -3.48387 3.37293 -10.30365 3.33591
TRAIT
ANXIETY
Equal
variances
assumed
1.515 .224 -1.108 50 .273 -3.57143 3.22221 -10.04343 2.90058
Equal
variances not
assumed
-1.058 36.019 .297 -3.57143 3.37568 -10.41749 3.27463
Young female shows greater mean BAI, state anxiety and Trait anxiety for female to (M = 15.80, SD = 8.64), (M = 39.48, SD = 11.06)
and (M = 45.00, SD= 10.20). In these, only BAI score shows sig. difference at sig. value of (t(50) = -2.18, p = 0.034<0.05) however, all the other
variables sig, value is above 0.05 demonstrate no sig difference exist between young male and female.
-----
H0: There is no significant mean difference between old and young males in terms of BDI and MOCA score.
H1: There is significant mean difference between old and young males in terms of BDI and MOCA score.
Group Statistics
Gender N Mean Std.
Deviation
Std. Error
Mean
BDI
Old Males 20 7.2000 5.07419 1.13462
Young
Males 21 8.2381 8.32409 1.81647
MoC
A
Old Males 20 27.0000 3.00876 .67278
Young
Males 21 27.7143 2.12468 .46364
SelfQ
Old Males 20 36.3000 8.49830 1.90028
Young
Males 21 38.7619 8.39586 1.83213
Independent Samples Test
Levene's Test for
Equality of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval
of the Difference
Lower Upper
BDI
Equal variances
assumed 3.820 .058 -.479 39 .634 -1.03810 2.16639 -5.42003 3.34384
Equal variances not
assumed -.485 33.313 .631 -1.03810 2.14171 -5.39388 3.31769

21
MoCA
Equal variances
assumed 2.200 .146 -.882 39 .383 -.71429 .81026 -2.35318 .92461
Equal variances not
assumed -.874 34.039 .388 -.71429 .81707 -2.37469 .94612
SelfQ
Equal variances
assumed .000 .998 -.933 39 .357 -2.46190 2.63885 -7.79948 2.87567
Equal variances not
assumed -.933 38.850 .357 -2.46190 2.63965 -7.80176 2.87795
Interpretation
It can be seen from table that there is no significant mean difference between old male and young male in terms of BDI and MOCA as
value of level of significance is 0.634>0.05 for BDI and same is 0.388>0.05 for MOCA. It can be seen from table that mean and standard
deviation values for male and female are not different from each other in case of BDI and same is observed in case of MOCA. In case of PRMQ
or self Q also there is no significant mean difference as value of level of significance is 0.357>0.05.
H0: There is no significant mean difference between BDI and MOCA as well as PRMQ in terms of old and young females.
H1: There is significant mean difference between BDI and MOCA as well as PRMQ in terms of old and young females.
Group Statistics
Gender N Mean Std.
Deviation
Std. Error
Mean
BDI
Old Females 32 5.8750 3.30932 .58501
Young
Females 31 12.0000 8.69483 1.56164
MoC
A
Old Females 32 28.2813 2.09815 .37090
Young
Females 31 27.7097 2.05254 .36865
SelfQ
Old Females 32 39.0938 9.98825 1.76569
Young
Females 31 41.0000 10.57040 1.89850
Independent Samples Test
Levene's Test for
Equality of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval
of the Difference
Lower Upper
BDI
Equal variances
assumed 28.409 .000 -3.717 61 .000 -6.12500 1.64764 -9.41965 -2.83035
Equal variances not
assumed -3.673 38.281 .001 -6.12500 1.66762 -9.50010 -2.74990
MoCA
Equal variances
assumed .009 .924 1.093 61 .279 .57157 .52313 -.47449 1.61763
Equal variances not
assumed 1.093 60.994 .279 .57157 .52294 -.47412 1.61726

22
SelfQ
Equal variances
assumed .788 .378 -.736 61 .465 -1.90625 2.59031 -7.08589 3.27339
Equal variances not
assumed -.735 60.523 .465 -1.90625 2.59267 -7.09145 3.27895
Interpretation
In case of BDI value of level of significance is 0.00<0.05 and this reflect that there is significant mean difference between old and young
females in terms of BDI score. It can be observed that mean and standard deviation values are almost same for both groups. On other hand, it can
be observed that in case of MOCA value of level of significance is 0.279>0.05 and this means that there is significant mean difference between
old and young females in terms of relevant criteria. It can be seen that mean and standard deviation values are almost same for both groups.
Hence, it can be concluded that results are different for both groups only in case of BDI. In case of PRMQ or self Q also value of level of
significance is 0.465>0.05 which means that there is no significant mean difference between both groups on PRMQ.
Correlation
Table 12 Correlation for Older adults and Youngsters Group
Age group Gender Age Marital
Status
Years of
education BAI STATE
ANXIETY
TRAIT
ANXIETY BDI MoCA SelfQ
(50-80)
older adults
Gender
Pearson
Correlation 1 .023 .044 -.032 -.116 .045 -.042 -.159 .248 .145
Sig. (2-
tailed) .874 .756 .824 .415 .753 .765 .259 .076 .305
N 52 52 52 52 52 52 52 52 52 52
Age
Pearson
Correlation .023 1 -.161 -.364** -.212 -.066 -.294* .113 -.065 .127
Sig. (2-
tailed) .874 .253 .008 .131 .644 .035 .427 .649 .368
N 52 52 52 52 52 52 52 52 52 52
Marital
Status
Pearson
Correlation .044 -.161 1 .044 .162 .310* .153 -.256 .201 .001
Sig. (2-
tailed) .756 .253 .758 .252 .025 .278 .067 .152 .993
N 52 52 52 52 52 52 52 52 52 52
Years of
education
Pearson
Correlation -.032 -.364*
* .044 1 .278* .080 .288* .038 -.176 .117
Sig. (2-
tailed) .824 .008 .758 .046 .571 .038 .791 .211 .407
N 52 52 52 52 52 52 52 52 52 52
BAI
Pearson
Correlation -.116 -.212 .162 .278* 1 .282* .381** .218 -.072 .283*
Sig. (2-
tailed) .415 .131 .252 .046 .042 .005 .120 .613 .042
N 52 52 52 52 52 52 52 52 52 52
STATE
ANXIETY
Pearson
Correlation
.045 -.066 .310* .080 .282* 1 .532** .220 .148 .292*

23
Sig. (2-
tailed) .753 .644 .025 .571 .042 .000 .116 .294 .036
N 52 52 52 52 52 52 52 52 52 52
TRAIT
ANXIETY
Pearson
Correlation -.042 -.294* .153 .288* .381** .532** 1 .450** -.063 .481**
Sig. (2-
tailed) .765 .035 .278 .038 .005 .000 .001 .658 .000
N 52 52 52 52 52 52 52 52 52 52
BDI
Pearson
Correlation -.159 .113 -.256 .038 .218 .220 .450** 1 .019 .186
Sig. (2-
tailed) .259 .427 .067 .791 .120 .116 .001 .892 .186
N 52 52 52 52 52 52 52 52 52 52
MoCA
Pearson
Correlation .248 -.065 .201 -.176 -.072 .148 -.063 .019 1 -.177
Sig. (2-
tailed) .076 .649 .152 .211 .613 .294 .658 .892 .209
N 52 52 52 52 52 52 52 52 52 52
SelfQ
Pearson
Correlation .145 .127 .001 .117 .283* .292* .481** .186 -.177 1
Sig. (2-
tailed) .305 .368 .993 .407 .042 .036 .000 .186 .209
N 52 52 52 52 52 52 52 52 52 52
(18-
25)Young
group Gender
Pearson
Correlation 1 .123 .183 .046 .295* .148 .155 .215 -.001 .114
Sig. (2-
tailed) .386 .194 .745 .034 .295 .273 .126 .994 .421
N 52 52 52 52 52 52 52 52 52 52
Age
Pearson
Correlation .123 1 .526** .183 -.229 -.235 -.283* -.138 -.014 -.082
Sig. (2-
tailed) .386 .000 .194 .102 .093 .042 .328 .920 .566
N 52 52 52 52 52 52 52 52 52 52
Marital
Status
Pearson
Correlation .183 .526** 1 -.125 -.060 -.104 -.262 -.038 .189 -.025
Sig. (2-
tailed) .194 .000 .378 .673 .462 .060 .787 .180 .859
N 52 52 52 52 52 52 52 52 52 52
Years of
education
Pearson
Correlation .046 .183 -.125 1 .006 .059 .196 -.015 .056 -.016
Sig. (2-
tailed) .745 .194 .378 .967 .677 .165 .918 .695 .912
N 52 52 52 52 52 52 52 52 52 52
BAI Pearson
Correlation
.295* -.229 -.060 .006 1 .654** .665** .723** -.209 .387**

24
Sig. (2-
tailed) .034 .102 .673 .967 .000 .000 .000 .137 .005
N 52 52 52 52 52 52 52 52 52 52
STATE
ANXIETY
Pearson
Correlation .148 -.235 -.104 .059 .654** 1 .766** .776** -.167 .326*
Sig. (2-
tailed) .295 .093 .462 .677 .000 .000 .000 .237 .018
N 52 52 52 52 52 52 52 52 52 52
TRAIT
ANXIETY
Pearson
Correlation .155 -.283* -.262 .196 .665** .766** 1 .789** -.035 .376**
Sig. (2-
tailed) .273 .042 .060 .165 .000 .000 .000 .807 .006
N 52 52 52 52 52 52 52 52 52 52
BDI
Pearson
Correlation .215 -.138 -.038 -.015 .723** .776** .789** 1 -.063 .428**
Sig. (2-
tailed) .126 .328 .787 .918 .000 .000 .000 .655 .002
N 52 52 52 52 52 52 52 52 52 52
MoCA
Pearson
Correlation -.001 -.014 .189 .056 -.209 -.167 -.035 -.063 1 -.277*
Sig. (2-
tailed) .994 .920 .180 .695 .137 .237 .807 .655 .047
N 52 52 52 52 52 52 52 52 52 52
SelfQ
Pearson
Correlation .114 -.082 -.025 -.016 .387** .326* .376** .428** -.277* 1
Sig. (2-
tailed) .421 .566 .859 .912 .005 .018 .006 .002 .047
N 52 52 52 52 52 52 52 52 52 52
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Pearson correlation shows relationship between two variables including both direction and strength of relationship. Trait anxiety shows
negative Pearson’s correlation coefficient of (r = -0.294, n = 52, p = 0.035<0.05) with age shows that elderly people with greater age perceive
lower threats. Similarly, for the younger, it is found to (r = -0.283, n = 52, p = 0.042<0.05) indicate significant negative relationship. It presents
that both the age group people anxiety level is negatively relate to the cost. Marital status shows moderate association with the state anxiety at
correlation of (r = 0.310, n = 52, p = 0.025<0.05). The findings of correlation presents that among elderly adults, separated, widowed or people
with domestic partner struggle with unpleasant emotional arouse than married or single people. Years of education shows moderate relationship
with BAI and Trait anxiety at correlation of (r = 0.278, n = 52, p = 0.046<0.05) and (r = 0.288, n = 52, p = 0.038<0.05) shows that greater, the
education years increase Beck Anxiety Inventory (BAI) and trait anxiety as well. However, looking to the younger group, correlation is not
significant as it is just (r = 0.006, n = 52, p = 0.967>0.05) and (r = 0.196, n = 52, p = 0.165>0.05) shows insignificant relationship.
Among elderly adults, BAI is also reflects positive relation with both the state and trait anxiety at (r = 0.282, n = 52, p = 0.042<0.05) and
(r = 0.381, n = 52, p = 0.005<0.05) shows significant relationship. Likewise, for the young people, BAI indicate correlation of (r = 654, n = 52, p
= 0.000<0.05), (r = 0.665, n = 52, p = 0.000<0.05) and (r = 0.723, n = 52, p = 0.000<0.05) with state anxiety, trait anxiety and BDI. Besides this,

25
trait anxiety among elderly tends to increase state anxiety and beck depression inventory (BDI) at the correlation coefficient of (r = 0.5532, n =
52, p = 000<0.05) and (r = 0.450, n = 52, p = 0.001<0.05) shows significant relationship. For younger group, trait anxiety with state anxiety &
BDI shows correlation of (r = .766, n = 52, p = 0.000<0.05) 0.766 and (r =0.789, n = 52, p = 0.000<0.05) as well shows strong relationship. It
demonstrates that higher the level of trait anxiety among youngsters leads to bring state anxiety and BDI issues.
Table 13 Correlation for old males
Gender Age Marital
Status
Years of
education
BAI STATE
ANXIETY
TRAIT
ANXIETY
BDI MoCA SelfQ
Gender
Pearson
Correlation .b .b .b .b .b .b .b .b .b .b
Sig. (2-tailed) . . . . . . . . .
N 20 20 20 20 20 20 20 20 20 20
Age
Pearson
Correlation .b 1 -.442 -.493* -.710** -.474* -.460* -.046 -.035 -.144
Sig. (2-tailed) . .051 .027 .000 .035 .041 .847 .883 .545
N 20 20 20 20 20 20 20 20 20 20
Marital Status
Pearson
Correlation .b -.442 1 .010 .264 .242 .158 -.305 .185 .305
Sig. (2-tailed) . .051 .967 .261 .303 .506 .191 .436 .192
N 20 20 20 20 20 20 20 20 20 20
Years of
education
Pearson
Correlation .b -.493* .010 1 .473* .067 .299 .027 -.255 .127
Sig. (2-tailed) . .027 .967 .035 .778 .201 .911 .278 .592
N 20 20 20 20 20 20 20 20 20 20
BAI
Pearson
Correlation .b -.710** .264 .473* 1 .535* .546* .117 -.311 .290
Sig. (2-tailed) . .000 .261 .035 .015 .013 .624 .182 .214
N 20 20 20 20 20 20 20 20 20 20
STATE
ANXIETY
Pearson
Correlation .b -.474* .242 .067 .535* 1 .687** .519* -.080 .269
Sig. (2-tailed) . .035 .303 .778 .015 .001 .019 .738 .252
N 20 20 20 20 20 20 20 20 20 20
TRAIT
ANXIETY
Pearson
Correlation .b -.460* .158 .299 .546* .687** 1 .568** -.257 .508*
Sig. (2-tailed) . .041 .506 .201 .013 .001 .009 .275 .022
N 20 20 20 20 20 20 20 20 20 20
BDI
Pearson
Correlation .b -.046 -.305 .027 .117 .519* .568** 1 -.110 .162
Sig. (2-tailed) . .847 .191 .911 .624 .019 .009 .643 .495
N 20 20 20 20 20 20 20 20 20 20
MoCA
Pearson
Correlation .b -.035 .185 -.255 -.311 -.080 -.257 -.110 1 -.292
Sig. (2-tailed) . .883 .436 .278 .182 .738 .275 .643 .211
N 20 20 20 20 20 20 20 20 20 20
SelfQ
Pearson
Correlation .b -.144 .305 .127 .290 .269 .508* .162 -.292 1
Sig. (2-tailed) . .545 .192 .592 .214 .252 .022 .495 .211
N 20 20 20 20 20 20 20 20 20 20
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
a. Gender = Old Males
b. Cannot be computed because at least one of the variables is constant.
Table 14 Correlations Coefficient for Old Females
Gender Age Marital
Status
Years of
education
BAI STATE
ANXIETY
TRAIT
ANXIETY
BDI MoCA SelfQ
Gender Pearson
Correlation .b .b .b .b .b .b .b .b .b .b
Sig. (2-tailed) . . . . . . . . .

26
N 32 32 32 32 32 32 32 32 32 32
Age
Pearson
Correlation .b 1 .014 -.293 .171 .052 -.207 .254 -.104 .241
Sig. (2-tailed) . .941 .104 .350 .778 .255 .160 .570 .184
N 32 32 32 32 32 32 32 32 32 32
Marital Status
Pearson
Correlation .b .014 1 .083 .049 .371* .155 -.192 .211 -.215
Sig. (2-tailed) . .941 .651 .789 .037 .398 .293 .246 .238
N 32 32 32 32 32 32 32 32 32 32
Years of
education
Pearson
Correlation .b -.293 .083 1 .018 .099 .282 .041 -.078 .126
Sig. (2-tailed) . .104 .651 .924 .591 .118 .822 .671 .492
N 32 32 32 32 32 32 32 32 32 32
BAI
Pearson
Correlation .b .171 .049 .018 1 .205 .224 .334 .329 .345
Sig. (2-tailed) . .350 .789 .924 .259 .218 .062 .066 .053
N 32 32 32 32 32 32 32 32 32 32
STATE
ANXIETY
Pearson
Correlation .b .052 .371* .099 .205 1 .503** .112 .275 .299
Sig. (2-tailed) . .778 .037 .591 .259 .003 .542 .127 .096
N 32 32 32 32 32 32 32 32 32 32
TRAIT
ANXIETY
Pearson
Correlation .b -.207 .155 .282 .224 .503** 1 .348 .133 .488**
Sig. (2-tailed) . .255 .398 .118 .218 .003 .051 .468 .005
N 32 32 32 32 32 32 32 32 32 32
BDI
Pearson
Correlation .b .254 -.192 .041 .334 .112 .348 1 .293 .273
Sig. (2-tailed) . .160 .293 .822 .062 .542 .051 .103 .131
N 32 32 32 32 32 32 32 32 32 32
MoCA
Pearson
Correlation .b -.104 .211 -.078 .329 .275 .133 .293 1 -.183
Sig. (2-tailed) . .570 .246 .671 .066 .127 .468 .103 .316
N 32 32 32 32 32 32 32 32 32 32
SelfQ
Pearson
Correlation .b .241 -.215 .126 .345 .299 .488** .273 -.183 1
Sig. (2-tailed) . .184 .238 .492 .053 .096 .005 .131 .316
N 32 32 32 32 32 32 32 32 32 32
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
a. Gender = Old Females
b. Cannot be computed because at least one of the variables is constant.
Considering gender, male’s age shows higher adverse relationship with the BAI, state anxiety and trait anxiety to (r = 0.710, n = 52, p =
0.000<0.05), (r = -0.474, n = 52, p = 0.035<0.05) and (r = -0.460, n = 52, p = 0.041<0.05) as well. However, female category’s age reflect
positive relationship with BAI and State anxiety to (r = 0.171, n = 52, p = 0.350>0.05) and (r = 0.052, n = 52, p = 0.758>0.05), still, does not
seems significant relationship. Male people state anxiety relationship with other components like BAI, trait anxiety and BDI also reflect
moderate relationship as their correlation coefficient lies in the band of (r = 0.535, n = 52, p = 0.015<0.05), (r = 0.687, n = 52, p = 0.001<0.05)
and (r = 0.519, n = 52, p = 0.019<0.05). However, under female, only the state anxiety and trait anxiety shows correlation of (r = 0.503, n = 52, p
= 0.003<0.05) means increase in either of both the anxiety level leads to increase other anxiety too. It presents that both male and female elderly
adults experiences higher state anxiety as a cause of higher trait anxiety. State anxiety is also relate to the SelfQ with correlation at (r = 0.299, n
= 52, p = 0.096>0.05) do not found significant while among male, BDI shows positive plus moderate relation with State and trait anxiety as their
correlation is determined to (r = 0.519, n = 52, p = 0.019<0.05) and (r = 0.568, n = 52, p = 0.009<0.05) respectively. However, female BDI &
state anxiety reflect very less relationship of (r = 0.112, n = 52, p = 0.542>0.05) which is not significant whilst trait anxiety shows correlation of
(r = 0.348, n = 52, p = 0.051>0.05).
Table 15 Correlation coefficient among various variables for Young Males
Gender Age Marital
Status
Years of
education
BAI STATE
ANXIETY
TRAIT
ANXIETY
BDI MoCA SelfQ

27
Gender
Pearson
Correlation .b .b .b .b .b .b .b .b .b .b
Sig. (2-tailed) . . . . . . . . .
N 21 21 21 21 21 21 21 21 21 21
Age
Pearson
Correlation .b 1 .b .552** -.323 -.203 -.197 -.223 -.157 -.110
Sig. (2-tailed) . . .009 .154 .378 .393 .332 .497 .636
N 21 21 21 21 21 21 21 21 21 21
Marital Status
Pearson
Correlation .b .b .b .b .b .b .b .b .b .b
Sig. (2-tailed) . . . . . . . . .
N 21 21 21 21 21 21 21 21 21 21
Years of
education
Pearson
Correlation .b .552** .b 1 -.176 .117 .178 -.035 .163 .297
Sig. (2-tailed) . .009 . .445 .614 .441 .881 .480 .192
N 21 21 21 21 21 21 21 21 21 21
BAI
Pearson
Correlation .b -.323 .b -.176 1 .745** .675** .829** -.234 .380
Sig. (2-tailed) . .154 . .445 .000 .001 .000 .307 .089
N 21 21 21 21 21 21 21 21 21 21
STATE
ANXIETY
Pearson
Correlation .b -.203 .b .117 .745** 1 .853** .768** -.158 .183
Sig. (2-tailed) . .378 . .614 .000 .000 .000 .493 .427
N 21 21 21 21 21 21 21 21 21 21
TRAIT
ANXIETY
Pearson
Correlation .b -.197 .b .178 .675** .853** 1 .864** .039 .238
Sig. (2-tailed) . .393 . .441 .001 .000 .000 .866 .298
N 21 21 21 21 21 21 21 21 21 21
BDI
Pearson
Correlation .b -.223 .b -.035 .829** .768** .864** 1 .041 .315
Sig. (2-tailed) . .332 . .881 .000 .000 .000 .861 .164
N 21 21 21 21 21 21 21 21 21 21
MoCA
Pearson
Correlation .b -.157 .b .163 -.234 -.158 .039 .041 1 -.242
Sig. (2-tailed) . .497 . .480 .307 .493 .866 .861 .290
N 21 21 21 21 21 21 21 21 21 21
SelfQ
Pearson
Correlation .b -.110 .b .297 .380 .183 .238 .315 -.242 1
Sig. (2-tailed) . .636 . .192 .089 .427 .298 .164 .290
N 21 21 21 21 21 21 21 21 21 21
**. Correlation is significant at the 0.01 level (2-tailed).
a. Gender = Young Males
b. Cannot be computed because at least one of the variables is constant.
Table 16 Correlation Coefficient among various variables for Young Female Group
Gender Age Marital
Status
Years of
education
BAI STATE
ANXIETY
TRAIT
ANXIETY
BDI MoCA SelfQ
Gender
Pearson
Correlation .b .b .b .b .b .b .b .b .b .b
Sig. (2-tailed) . . . . . . . . .
N 31 31 31 31 31 31 31 31 31 31
Age
Pearson
Correlation .b 1 .599** .045 -.270 -.297 -.395* -.148 .057 -.092
Sig. (2-tailed) . .000 .809 .142 .104 .028 .428 .761 .622
N 31 31 31 31 31 31 31 31 31 31
Marital Status
Pearson
Correlation .b .599** 1 -.159 -.173 -.184 -.432* -.103 .252 -.056
Sig. (2-tailed) . .000 .394 .351 .323 .015 .582 .172 .763
N 31 31 31 31 31 31 31 31 31 31
Years of
education
Pearson
Correlation .b .045 -.159 1 .095 .019 .210 -.021 .002 -.146
Sig. (2-tailed) . .809 .394 .610 .918 .256 .911 .991 .434

28
N 31 31 31 31 31 31 31 31 31 31
BAI
Pearson
Correlation .b -.270 -.173 .095 1 .555** .635** .619** -.206 .382*
Sig. (2-tailed) . .142 .351 .610 .001 .000 .000 .266 .034
N 31 31 31 31 31 31 31 31 31 31
STATE
ANXIETY
Pearson
Correlation .b -.297 -.184 .019 .555** 1 .675** .778** -.177 .401*
Sig. (2-tailed) . .104 .323 .918 .001 .000 .000 .341 .025
N 31 31 31 31 31 31 31 31 31 31
TRAIT
ANXIETY
Pearson
Correlation .b -.395* -.432* .210 .635** .675** 1 .732** -.100 .467**
Sig. (2-tailed) . .028 .015 .256 .000 .000 .000 .592 .008
N 31 31 31 31 31 31 31 31 31 31
BDI
Pearson
Correlation .b -.148 -.103 -.021 .619** .778** .732** 1 -.134 .469**
Sig. (2-tailed) . .428 .582 .911 .000 .000 .000 .471 .008
N 31 31 31 31 31 31 31 31 31 31
MoCA
Pearson
Correlation .b .057 .252 .002 -.206 -.177 -.100 -.134 1 -.303
Sig. (2-tailed) . .761 .172 .991 .266 .341 .592 .471 .098
N 31 31 31 31 31 31 31 31 31 31
SelfQ
Pearson
Correlation .b -.092 -.056 -.146 .382* .401* .467** .469** -.303 1
Sig. (2-tailed) . .622 .763 .434 .034 .025 .008 .008 .098
N 31 31 31 31 31 31 31 31 31 31
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
a. Gender = Young Females
b. Cannot be computed because at least one of the variables is constant.
Among youngsters, male BAI shows strong relationship with the BDI at correlation of (r = 0.829, n = 52, p = 0.000<0.05) and for both
state and trait anxiety score, the correlation is determined to (r = 0.745, n = 52, p = 0.000<0.05) and (r = 0.675, n = 52, p = 0.001<0.05). It means
that male with greater beck anxiety inventory score experiences higher depression (BDI) or vice-versa. Similarly, young female BAI score found
correlation of (r = 0.555, n = 52, p = 0.001<0.05), (r = 0.635, n = 52, p = 0.000<0.05) and (r = 0.619, n = 52, p = 0.000<0.05) with state anxiety,
trait anxiety and BDI. Male BDI score evident strong association with the trait anxiety (r = 0.864, n = 52, p = 0.000<0.05) and state and trait
anxiety found relation of (r = 0.853, n = 52, p = 0.000<0.05) indicate that these anxiety score are closely relate to each other and change in one
will definitely bring change in related variable. However, in female, BDI shows strong relation with the state anxiety (r = 0.778, n = 52, p =
0.000<0.05) while with BAI and trait anxiety, it indicate correlation of (r = 0.619, n = 52, p = 0.000<0.05).619 and (r = 0.732, n = 52, p =
0.000<0.05) respectively. Thus, the finding present distinct relation in anxiety scores for both the young male and young female category.
DISCUSSION
As per the results, it is determined that BAI, state anxiety, trait anxiety and BDI shows significant difference in respect to both the
younger and elderly adults. Anxiety shows favourable relation with depression in relation to male, as there is high degree of relationship between
relevant parameters. However, in case of young age people it is seen that such kind of strong relation does not exist and it can be said that
relevant sort of people are less affected by depression. There is difference between male and female in respect to level of anxiety that is observed
in case of both sort of people with increase in age. It can be observed that with increase in age anxiety level decline in case of male. It can be
said that tolerance power is high in case of male. On other hand, in case of females it is identified that with increase in age anxiety level also
elevate at fast rate. It is also identified during analysis that if women are suffered from anxiety then in that case there are chances that they may
go in depression relative to male. Hence, women are more at risk then male.
Petkus and et.al. (2017), findings reported that anxiety and cognitive performance are dynamically related to each other and greater the
level of anxiety leads to decline processing speed over the period and worsen attention. However, reverse direction is significant in slow
processing speed, poor non-verbal communication and working memory. Thus, the finding suggested that anxiety and cognitive performance
among elder adults follows a complex and bidirectional relationship.

29
Dotson and et.al., (2015), researched interactive impact of both the anxiety and depression on cognitive functioning among both the
young and older people. According to the results of the study, both the factors leads to decline cognitive deficit, more importantly, among elder
people. The current research evidenced that that there is less relationship between BAI and MoCA as well as trait anxiety for elderly people. On
other hand, in case of young people to some extent inverse trends are observed. These facts reflect that there is impact on anxiety on depression
level but this thing is observed at big level in case of young people then old people.
Dotson and et.al. (2015), research study reflected that total depressive symptoms, depressed mood and others is highly related with
deficit in speed, working memory and executive functions among elder adults. However, our findings did not support it as in this, MoCA does
not demonstrates significant association with BDI for both the younger and older population.
T-test results found significant difference in the anxiety score including BAI, BDI, state anxiety and trait anxiety of both the age group as
it is greater in the young age people. However, there is no impact of gender on the anxiety score of people lies in the elder adults group at sig
value above 0.05. By contrast, young age female shows greater BAI than female while other factors including state anxiety, trait anxiety, BDI,
MoCA and selfQ did not found sig difference.
Study limitation and recommendation
Major limitation of present research study is that sample of 100 people is taken and there is possibility that respondent’s responses are not
recorded properly. If same happened then in that case research may go in wrong direction. It is recommended that researcher must before finally
entering data in SPSS must identify whether all questions response are in same direction.
---

30
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