Anxiety, Depression, Cognitive Performance, And Cognitive Decline In Young And Elderly Adults Assignment Sample

  • 95032+ Project Delivered
  • 503+ Experts 24x7 Online Help
Download Full Sample
Downloads 13257 Words
This is a Sample Assignment Submitted by Student. Contact our Experts for 100% Plagiarism Free Assignment Solutions

Anxiety, Depression, Cognitive Performance, And Cognitive Decline In Young And Elderly Adults

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 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: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
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 (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 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) 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 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.

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).

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 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.

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.

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).

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).

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 1Descriptive 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

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 2Gender 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 3 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 3Level 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

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 4Marital 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

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.

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 6Mean 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.

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

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

MoCA

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

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

MoCA

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

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 0.000.05 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 12Correlation 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*

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**

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 cshows 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, 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 13Correlation 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 14Correlations 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)

.

.

.

.

.

.

.

.

.

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 15Correlation coefficient among various variables for Young 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

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 16Correlation 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

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.

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.

Dotson and et.al., (2015), researched interactive impact of both the anxiety and depression on cognitive functioning among both the young and older people.

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.

REFERENCES

Aalto, A-M., Elovainio, M., Kivimäki, M., Uutela, A. & Pirkoa, S. (2012). The Beck depression inventory and general health questionnaire as measures of depression in the general population: A validation study using the composite international diagnostic interview as the gold standard. Psychiatry Research, 197(2012), 163-171. DOI: 10.1016/j.psychres.2011.09.008

American Psychiatric Association (APA). (2013). Diagnostic and statistical manual of mental disorders, 5th ed. Washington DC: American Psychiatric Association.

Astell, A. J., Gomersall, T., Blewett, C. & Smith, S. (2016). Is a diagnosis of MCI a blessing or a curse? Alzheimer’s and Demetia, 12(7), P1011-P1012. DOI: 10.1016/j.jalz.2016.06.2083

Backhaus, J., Junghanns, K., Broocks, A., Riemann, D & Hohagen, F. (2002). Test-retest reliability and validity of the Pittsburgh sleep quality index in primary insomnia. Journal of Psychiatric Research, 53(3), 737-740. DOI: 10.1016/S0022-3999(02)00330-6

Beaudreau, S. A., & O'hara, R. (2008). Late-life anxiety and cognitive impairment: A review.  The American Journal of Geriatric Psychiatry,  16(10), 790-803. DOI: 10.1097/JGP.0b013e31817945c3

Beaudreau, S. A., & O'hara, R. (2009). The association of anxiety and depressive symptoms with cognitive performance in community-dwelling older adults.  Psychology and aging,  24(2), 507. DOI: 10.1037/a0016035

Beck, A. T., Epstein, N., Brown, G., Steer, R. A. (1988). An inventory for measuring clinical anxiety: Psychometric properties. Journal of Consulting and Clinical Psychology, 56, 893-897.

Bhalla, R. K., Butters, M. A., Becker, J. T., Houck, P. R., Snitz, B. E., Lopez, O. L., ... & Reynolds, C. F. (2009). Patterns of mild cognitive impairment after treatment of depression in the elderly.  The American Journal of Geriatric Psychiatry,  17(4), 308-316. DOI: 10.1097/JPG.0b013e318190b8d8

Bierman, E. J. M., Comijs, H. C., Jonker, C., & Beekman, A. T. F. (2005). Effects of anxiety versus depression on cognition in later life.  The American Journal of Geriatric Psychiatry,  13(8), 686-693. DOI: 10.1097/00019442-200508000-00007

Bierman, E. J., Comijs, H. C., Rijmen, F., Jonker, C., & Beekman, A. T. (2008). Anxiety symptoms and cognitive performance in later life: Results from the longitudinal aging study Amsterdam.  Aging and Mental Health,  12(4), 517-523. DOI: 10.1080/13607860802224276

Biringer, E., Mykletun, A., Dahl, A. A., Smith, A. D., Engedal, K., Nygaard, H. A., & Lund, A. (2005). The association between depression, anxiety, and cognitive function in the elderly general population—the Hordaland Health Study.  International journal of geriatric psychiatry,  20(10), 989-997. DOI: 10.1002/gps.1390

Bondi, M., Edmonds, E., Jak, A., Clark, L., Delano-Wood, L., McDonald, C.…Salmon, D. (2014). Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. Journal of Alzheimer's Disease, 42(1), 275-289. DOI: 10.3233/JAD-140276

Borg, C., Leroy, N., Favre, E., Laurent, B. &Thomas-Antérion, C. (2011). How emotional pictures influence visuospatial binding in short-term memory in ageing and Alzheimer’s disease? Brain and Cognition, 76, 20-25. DOI: 10.1016/j.bandc.2011.03.008

Boyle, L. L., Lyness, J. M., Duberstein, P. R., Karuza, J., King, D. A., Messing, S., & Tu, X. (2010). Trait neuroticism, depression, and cognitive function in older primary care patients.  The American Journal of Geriatric Psychiatry,  18(4), 305-312. DOI: 10.1097/JPG.0b013e3181c2941b

Bryant, C., Jackson, H., & Ames, D. (2008). The prevalence of anxiety in older adults: methodological issues and a review of the literature.  Journal of affective disorders,  109(3), 233-250. DOI: 10.1016/j.jad.2007.11.008

Buysse, D. J., Reynolds III, C. F., Monk, T. F., Berman, S. R. & Kupfer, D. J. (1989). The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193-213. DOI: 10.1016/0165-1781(89)90047-4

Caracciolo, B., Gatz, M., Xu, W., Pedersen, N., & Fratiglioni, L. (2012). Differential distribution of subjective and objective cognitive impairment in the population: A nation-wide twin-study. Journal of Alzheimer's Disease, 29(2), 393-403. DOI: 10.3233/JAD-2011-111904

Chainay, H., Michael, G. A., Vert-pré, M., Landré, L. & Plasson, A. (2012). Advances in Cognitive Psychology, 8(3), 255-266. DOI: 10.5709/acp-0121-1

Charles, S. T., Mather, M. & Carstensen, L. L. (2003). Aging and emotional memory: The forgettable nature of negative images for older adults. Journal of Experimental Psychology: General, 132(2), 310-324. DOI: 10.1037/0096-3445.132.2.310

Cooper, C., Sommerlad, A., Lyketsos, C. G. & Livingston, G. (2015). Modifiable predictors of dementia in mild cognitive impairment: A systematic review and meta-analysis. The American Journal of Psychiatry, 172(4), 323-334. DOI: 10.1176/1ppi.ajp.2014.14070878

Crawford, J.R., Smith, G., Maylor, E., Della Salla, S. & Logie, R. H. (2003). The prospective and retrospective memory questionnaire (PRMQ): Normative data and latent structure in a large non-clinical sample. Memory, 11(3), 261-275. DOI: 10.1080/09658210244000027

Erikson, E. (1982). The life cycle completed: A review. New York, NY: Norton.

Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory.  Emotion,  7(2), 336. DOI: 10.1037/1528-3542.7.2.336

Farinde, A. (2013). The Beck Depression Inventory. The Pharma Innovation Journal, 2(2013), 56-62. Available from: http://www.thepharmajournal.com

Fauzan, N. & Amran, N. H. (2015). Brain dynamics of mild cognitive impairment (MCI) from EEG features. Procedia Social and Behavioral Sciences, 165(2015), 284-290. DOI: 10.1016/j.sbspro.2014.12.633

Fischer, M., Cruickshanks, K., Schubert, C., Pinto, A., Carlsson, C., Klein, B., Klein, R., & Tweed, T. (2016). Age-related sensory impairments and risk of cognitive impairment. Journal of American Geriatrics Society, 64(10), 1981-1987. DOI: 10.1111/jgs.14308

Genziani, M., Stewart, R., Béjot, Y., Amieva, H., Artero, S., & Ritchie, K. (2013). Subjective memory impairment, objective cognitive functioning and social activity in French older people: Findings from the Three Cities study. Geriatrics & Gerontology International, 13(1), 139-145. DOI: 10.1111/j.1447-0594.2012.00873.x

Grenier, S., Préville, M., Boyer, R., O’Connor, K., Béland, S. G., Potvin, O., ... & Brassard, J. (2011). The impact of DSM-IV symptom and clinical significance criteria on the prevalence estimates of subthreshold and threshold anxiety in the older adult population.  The American Journal of Geriatric Psychiatry,  19(4), 316-326. DOI: 10.1097/JGP.0b013e3181ff416c

Guillo-Benarous, F., Vengassery, A., Ghimire, S., Xu, J., Torossian, C., & Reisberg, B. (2014). Evaluation of symptoms in persons with subjective cognitive impairment. Alzheimer's and Dementia, 10(4), P813-P814. DOI: 10.1016/j.jalz.2014.05.1599

Gum, A. M., King-Kallimanis, B., & Kohn, R. (2009). Prevalence of mood, anxiety, and substance-abuse disorders for older Americans in the national comorbidity survey-replication.  The American Journal of Geriatric Psychiatry,  17(9), 769-781. DOI: 10.1097/JGP.0b013e3181ad4f5a

Hearn, S., Saulnier, G., Strayer, J., Glenham, M., Koopman, R., & Marcia, J. E. (2012). Between integrity and despair: Toward construct validation of Erikson’s eighth stage. Journal of Adult Development, 19(1), 1-20. DOI: 10.1007/s10804-011-9126-y

Herrmann, L. L., Goodwin, G. M., & Ebmeier, K. P. (2007). The cognitive neuropsychology of depression in the elderly.  Psychological medicine,  37(12), 1693-1702. DOI: 10.1017/S0033291707001134

Hill, N. L., Mogle, M., Wilson, R., Munoz, E., DePasquale, N., Yevchak, A.M. & Parisi, J. M. (2016). Subjective cognitive impairment and affective symptoms: A systematic review. The Gerontologist, 56(6), e109-e127. DOI: 10.1093/geront/gnw091

Hogan, M. J. (2003). Divided attention in older but not younger adults is impaired by anxiety.  Experimental aging research,  29(2), 111-136. DOI: 10.1080/03610730303712

Hollands, S., Lim, Y., Buckley, R., Pietrzak, R., Snyder, P., Ames, D.… Maruff, P. (2015). Amyloid-β Related Memory Decline is not Associated with Subjective or Informant Rated Cognitive Impairment in Healthy Adults. Journal of Alzheimer's Disease, 43(2), 677-686. DOI: 10.3233/JAD-140678

Julayanont, P., Phillips, N., Chertkow, H. & Nasreddine, Z. A. (2013). The Montreal Cognitive Assessment (MoCA): Concept and clinical review. In: Cognitive Screening Instruments: A Practical Approach. New York, NY: Springer-Verlag, pp. 111-152.

Julian, L. (2011). Measures of anxiety state-trait anxiety inventory (STAI), Beck anxiety inventory (BAI), and hospital anxiety and depression scale-anxiety (HADS-A). Arthritis Care & Research, 63(S11), S467–S472.

Kauth, M., & Zettle, R. (1990). Validation of depression measures in adolescent populations. Journal of Clinical Psychology, 46(3), 291-295. DOI: 10.1002/1097-4679(199005)46:33.0.CO;2-S

Kensinger, E. A. & Corkin, S. (2003). Effect of negative emotional content on working memory and long-term memory. Emotion, 3(4), 378-393. DOI: 10.1037/1528-3542.3.4.378

Lezak, M. D., Howieson, D. B. & Loring, D. W. (2004). Neuropsychological assessment. 4th ed. New York NY: Oxford University Press.

Lin, F. R., Metter, E. J., O’Brien, R. J. et al. (2011). Hearing loss and incident dementia. Archives of Neurology, 68, 154-168.

Lin, F. R., Yaffe, K., Xia, J., Xue, Q. L., Harris, T. B., Purchase-Helzner, E., ... & Health ABC Study Group, F. (2013). Hearing loss and cognitive decline in older adults. JAMA Internal Medicine, 173(4), 293-299.

Lockwood, K. A., Alexopoulos, G. S., Kakuma, T., & Van Gorp, W. G. (2000). Subtypes of cognitive impairment in depressed older adults.  The American Journal of Geriatric Psychiatry,  8(3), 201-208. DOI: 10.1097/00019442-2000080000-00004

Luck, T. Röhr, Jessen, F., Villringer, A., Angermeyer, M. C. & Riedel-Hller, S. G. (2015). Mortality in individuals with subjective cognitive decline: Results of the Leipzig longitudinal study of the aged (LEILA75+). Journal of Alzheimer’s Disease, 48(Suppl 1), S43-S55. DOI: 10.3233/JAD-150090

Mah, L., Binns, M. A., Steffens, D. C (2015). Anxiety symptoms in amnestic mild cognitive impairment are associated with medial temporal atrophy and predict conversion to Alzheimer disease. The American Journal of Geriatric Psychiatry, 23(5), 466-476. DOI: 10.1016/j.jagp.2014.10.005

Martinez-Galindo, J. G. & Cansino, S. (2015). Positive and negative emotional contexts unevenly predict episodic memory. Behavioral Brain Research, 291, 89-102. DOI: 10.1016/j.bbr.2015.05.018

McDowell, I. (2006). The state-trait anxiety inventory (C.H. Spielberger, 1968, 1977). In: Measuring health: A guide to rating scales and questionnaires. New York, NY Oxford University Press.

McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., ... & Mohs, R. C. (2011). The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease.  Alzheimer's & dementia,  7(3), 263-269. DOI: 10.1016/h.jalz.2011.03.005

Mirza, S., Ikram, M., Bos, D., Mihaescu, R., Hofman, A., & Tiemeier, H. (2017). Mild cognitive impairment and risk of depression and anxiety: A population-based study. Alzheimer's and Dementia, 13(2), 130-139. DOI: 10.1016/j.jalz.2016.06.2361

Mitchell, A., Beaumont, H., Ferguson, D., Yadegarfar, M., & Stubbs, B. (2014). Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: meta‐analysis. Acta Psychiatrica Scandinavica, 130(6), 439-451. DOI: 10.1111/acps.12336

Molnár, M., Tóth, B., Boha, R., Gaál, Zs. A., Kardos, Zs., File, B. & Stam, C. J. (2013). Aging effects on ERP correlates of emotional word discrimination. Clinical Neurophysiology, 124, 1986-1994. DOI: 10.1016/j.clinphy.2013.04.017

Molnár, M., Tóth, B., Boha, R., Gaál, Zs. A., Kardos, Zs., File, B. & Stam, C. J. (2013). Aging effects on ERP correlates of emotional word discrimination. Clinical Neurophysiology, 124, 1986-1994. DOI: 10.1016/j.clinphy.2013.04.017

Moretti, D., Zanetti, O., Binetti, G., & Frisoni, G. (2012). Quantitative EEG markers in mild cognitive impairment: Degenerative versus vascular brain impairment. International Journal of Alzheimer's Disease, 2012, Article ID: 917537. DOI: 10.1155/2012/917537

Olichney, J. M., Taylor, J. R., Hillert, D. G., Chan, S-H., Salmon, D. P., Gatherwright, J., Iragui, V. J. & Kutas, M. (2010). fMRI congruous word repetition effects reflect memory variability in normal elderly. Neurobiology of Aging, 31, 1975-1990. DOI: 20.1016/j.neurobiolaging.2008.10.010

Pelak, V. S. (2010). Ocular motility of aging and dementia. Current Neurology and Neuroscience Reports, 10, 440-447. DOI: 10.1007/s11910-010-0137-z

Penk, W., & Robinowitz, R. (1987). Confirmatory factor analysis of the short form Beck Depression Inventory in elderly community samples. Journal of Clinical Psychology, 43(1), 111-118. DOI: 10.1002/1097-4679(198701)43:13.0.CO;2-S

Pennington, C., Hayre, A., Newson, M. & Coulthard, E. (2015). Functional cognitive disorder: A common cause of subjective cognitive symptoms. Journal of Alzheimer’s Disease, 48(Suppl 1), S19-S24. DOI: 10.3233/JAD-150182

Perrotin, A., DeFlores, R., Lamberton, F., Poisnel, G., LaJoie, R., De La Sayette, V….Chételat, G. (2015). Hippocampal subfield volumetry and 3D surface mapping in subjective cognitive decline. Journal of Alzheimer’s Disease, 48(Suppl 1), S141-S150. DOI: 10.3233/JAD-150087

Petersen, R., Caracciolo, B., Brayne, C., Gauthier, S., Jelic, V., & Fratiglioni, L. (2014). Mild cognitive impairment: a concept in evolution. Journal of Internal Medicine, 275(3), 214-228. DOI: 10.1111/joim.12190

Pietrzak, R. H., Maruff, P., Woodward, M., Fredrickson, J., Fredrickson, A., Krystal, J. H., ... & Darby, D. (2012). Mild worry symptoms predict decline in learning and memory in healthy older adults: A 2-year prospective cohort study.  The American Journal of Geriatric Psychiatry,  20(3), 266-275. DOI: 10.1097/JGP.0b013e3182107e24

Reijnders, J., van Heugten, C., & van Boxtel, M. (2013). Cognitive interventions in healthy older adults and people with mild cognitive impairment: A systematic review. Ageing Research Reviews, 12(1), 263-275. DOI: 10.1016/j.arr.2012.07.003

Scogin, F., Beutler, L., Corbishley, A., & Hamblin, D. (1988). Reliability and validity of the short form beck depression inventory with older adults. Journal of Clinical Psychology, 44(6), 853-857. DOI: 10.1002/1097-4679(198811)44:63.0.CO;2-7

Smith, G., Della Sala, S. Logie, R. H. & Maylor, E.A. (2000). Prospective and retrospective memory in normal ageing and dementia: A questionnaire study. Memory, 8, 311-321.

Snitz, B. E., Lopez, O. L., McDade, E., Becker, J. T., Cohen, A. D., Price, J. C….Klunk, W. E. (2015) Amyloid-beta imaging in older adults presenting to a memory clinic with subjective cognitive decline. Journal of Alzheimer’s Disease, 48(Suppl 1), S151-S159. DOI: 10.3233/JAD-150113

Takeda, M., Morihara, T., Okochi, M., Sadik, G., & Tanaka, T. (2008). Mild cognitive impairment and subjective cognitive impairment. Psychogeriatrics, 8(4), 155-160. DOI: 10.1111/j.1479-8301.2008.00258.x

Tales, A., Bayer, A., Krajcick, S. & Jellinger, K. A. (2012). Mild cognitive impairment: Beyond memory dysfunction. International Journal of Alzheimer’s Disease, 2012, article ID:262305. DOI: 10.1155/2012/262305

Tales, A., Jessen, F., Butler, C., Wilcock, G., Phillips, J. & Bayer, T. (2015). Subjective cognitive decline. Journal of Alzheimer’s Disease, 48(Suppl 1), S1-S3. DOI: 10.3233/JAD-150719

Wetherell, J. L., Reynolds, C. A., Gatz, M., & Pedersen, N. L. (2002). Anxiety, cognitive performance, and cognitive decline in normal aging.  The Journals of Gerontology Series B: Psychological Sciences and Social Sciences,  57(3), P246-P255. DOI: 10.1093/geronb/57.3.P246

Wetherell, J., & Gatz, M. (2005). The Beck Anxiety Inventory in older adults with generalized znxiety disorder. Journal of Psychopathology and Behavioral Assessment, 27(1), 17-24. DOI: 10.1007/s10862-005-3261-3

Wolfe, J. M. (1998). Visual search. Harvard University. Available from: http://search.bwh.harvard.edu/new/pubs/the_review.pdf

Yates, J. A., Clare, L. & Woods, R. T. (2017). What is the relationship between health, mood, and mild cognitive impairment? Journal of Alzheimer’s Disease, 55, 1183-1193. DOI: 10.3233/JAD-160611

Yeh, Y. C., Tsang, H. Y., Lin, P. Y., Kuo, Y. T., Yen, C. F., Chen, C. C., ... & Chen, C. S. (2011). Subtypes of mild cognitive impairment among the elderly with major depressive disorder in remission.  The American Journal of Geriatric Psychiatry,  19(11), 923-931. DOI: 10.1097/JGP.0b013e318202clc6

Zimmerman, C. A. & Kelley, C. M. (2010). “I’ll remember this!” Effects of emotionality on memory prediction versus memory performance. Journal of Memory and Language, 62, 240-253. DOI: 10.1016/j.jml.2009.11.004

Beaudraeau,A. S. and Hara, R., 2009. The association of Anxiety and Depressive Symptoms with cognitive performance in community-dwelling older adults. [Online]. Available through: < https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2725021/>.

Cisler, M. J., and Olatunji, O., B., 2013. Emotion Regulation and Anxiety Disorder. [Online]. Available through: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3596813/#R44>.

Ganguli, M., 2009. Depression, Cognitive Impairment and Dementia: Why Should Clinicians care about the web of causation?. [Online]. Available through: < https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3038544/>.

Kashdan, T. B,, Zvolensky M. J, and McLeish A. C J., 2008. Anxiety sensitivity and affect regulatory strategies: individual and interactive risk factors for anxiety-related symptoms. Anxiety Disord. 22(3). pp.429-40.

Peterson, D., 2015. Connection Found Between Anxiety, Amyloid and Cognitive Change. [Online]. Available through: < https://www.cogstate.com/connection-found-anxiety-amyloid-cognitive-change/>.

Trachy, N., 2015. Depresion nd cognitive dysfunction. [Online]. Available through: < https://www.healthyplace.com/depression/symptoms/depression-and-cognitive-dysfunction/>.
Bowman, L., 2017. The Link Between Depression and Anxiety. [Online]. Available through: < http://metro.co.uk/2017/10/04/the-link-between-depression-and-anxiety-6942401/>.

Tracy, N., 2015. Relationship between Depression and Anxiety. [Online]. Available through: < https://www.healthyplace.com/depression/anxiety-and-depression/relationship-between-depression-and-anxiety/>.

Morimoto, S. S. and et.al., 2015. Cognitive Impairment in Depressed older adults. [Online]. Available through: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4376269/>.

Vytal, E. K. and et.al., 2013. The complex interaction between anxiety and cognition. [online]. Available through: < https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3610083/>.

Yang, Y. and et.al., 2015. Cognitive impairment in generalized anxiety disorder revealed by event related potential N270. [Online]. Available through: < https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4461089/>.

Robinson, J. O. and et.al., 2013. The impact of anxiety upon cognition: perspectives from human threat of shock studies. [Online]. Available through: < https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3656338/>.

Bardrakalimuthu, R. V. and Tarbuck, F. A., 2012. Anxiety: a hidden element in dementia. [Online]. Available through: < http://apt.rcpsych.org/content/18/2/119>.

Liang, Y., Wang, L. and Zhu, J., 2016. Factor structure and psychometric properties of Chinese version of Beck Anxiety Inventory in Chinese doctors.  Journal of health psychology. p.1359105316658971.

de Oliveira, G.N. and et.al.,2014. Screening for depression in people with epilepsy: comparative study among neurological disorders depression inventory for epilepsy (NDDI-E), hospital anxiety and depression scale depression subscale (HADS-D), and Beck depression inventory (BDI).  Epilepsy & Behavior. 34. pp.50-54.

Julayanont, P. and Nasreddine, Z.S., 2017. Montreal Cognitive Assessment (MoCA): concept and clinical review. In  Cognitive screening instruments. Springer International Publishing. pp. 139-195

Mefoh, P.C. and Ezeh, V.C., 2016. Effect of Cognitive Style on Prospective-Retrospective Memory Slips.  Swiss Journal of Psychology. 15(3). Pp.18-39.

Dennis, C.L., Coghlan, M. and Vigod, S., 2013. Can we identify mothers at-risk for postpartum anxiety in the immediate postpartum period using the State-Trait Anxiety Inventory?.  Journal of Affective Disorders. 150(3). pp.1217-1220.

Dotson, M. V. and et.al., 2015. Unique and interactive effect of anxiety and depressive symptoms on cognitive and brain function in young and older adults. [Online]. Available through: < https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222514/>.

Petkus, A. J. and et.al., 2017. Temporal dynamics of cognitive performance and anxiety across older adulthood.  Psychology and Aging. 32(3). pp.278-292.

Save Time & improve Grades

just share your requirements and get customized solutions on time

- +
35% Off
£ 6.69
Estimated Cost
£ 4.35
Your Paper will Include
  • Title Page
  • Table of Contents
  • Body
  • Bibliography & References
  • Any Additional requirements that you specify!
Free Product features
  • Free Product Features
  • Free Quality Assurance Reports
  • Grammar Report
  • Compliance Report
  • Plagiarism Report
Free Service Features
  • Free Order Management
  • Free E-Guides
  • Free 24x7 exclusive customer support
Free Assignment Sample on New assignment help
Get best price for your work

offer valid for limited time only*

in a hurry-get a callback